Carbon Chemistry analysis: seasonality and long trends
Ana Palacio-Castro
2023-03-10
Libraries and settings
1. Import and format carbon chemistry data
Seasonality
Winter: Jan-Mar
Spring: Apr-Jun
Summer: Jul-Sep
Fall: Oct-Dec
Wet: May - October
Dry: November - April
2. Check data available
2.1 Locations (all samples)
Figure Supp: Map of the sites where individual samples were collected
Notes: * Check location of individual SiteIDs
2.2 Average GPS point location
Flag samples with coordinates that seem off from the permanent station
# Coordinates<-select(FLK.data, "CTDID", "Latitude", "Longitude", "SiteID")
# Coordinates<-join(Coordinates, WS.GPS.Sites,
# type = "left", by="SiteID")
# Coordinates$Lat_diff <- abs(Coordinates$Latitude-Coordinates$Lat)
# Coordinates$Lon_diff <- abs(Coordinates$Longitude-Coordinates$Lon)
# Check.coordinates1 <- Coordinates[(Coordinates$Lat_diff>0.02), ]
# Check.coordinates2 <- Coordinates[(Coordinates$Lon_diff>0.02), ]
# Check.coordinates <- rbind(Check.coordinates1, Check.coordinates2)
# Check.coordinates <- unique(Check.coordinates)
# #write.csv(Check.coordinates, "FLK_results/2_WrongGPS_points.csv", row.names=F)- Figure 1: Stations
Figure 1: Location of the permanent sampling stations along the Florida Reef Tract. The black boxes delimitate the biogeographical regions. The color of the stations represents their position along the shelf.
2.3 Number of samples
By zone and location
| Sub_region | Inshore | Mid channel | Offshore | Oceanic |
|---|---|---|---|---|
| BB | 83 | 75 | 82 | |
| UK | 105 | 70 | 110 | 36 |
| MK | 124 | 126 | 127 | 78 |
| LK | 133 | 136 | 168 | 85 |
By year and sub_region
| Sub_region | 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| BB | 4 | 11 | 3 | 6 | 30 | 36 | 30 | 35 | 24 | 24 | 37 |
| UK | 7 | 15 | 3 | 8 | 45 | 51 | 42 | 44 | 30 | 31 | 45 |
| MK | 15 | 39 | 9 | 11 | 55 | 66 | 53 | 56 | 43 | 44 | 64 |
| LK | 18 | 51 | 9 | 11 | 58 | 72 | 59 | 76 | 53 | 45 | 70 |
By climatological season and region
| Sub_region | Fall | Spring | Winter | Summer |
|---|---|---|---|---|
| LK | 149 | 133 | 123 | 117 |
| MK | 132 | 119 | 95 | 109 |
| UK | 88 | 81 | 72 | 80 |
| BB | 68 | 60 | 52 | 60 |
By Month-year
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Months |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | 37 | 19 | 6 | 35 | January | ||||||
| 16 | 22 | 37 | February | ||||||||
| 24 | 2 | 38 | 35 | 33 | March | ||||||
| 23 | 38 | 15 | 35 | 37 | April | ||||||
| 38 | 16 | 28 | May | ||||||||
| 20 | 24 | 38 | 38 | 35 | 8 | June | |||||
| 37 | 38 | 17 | 36 | 25 | July | ||||||
| 19 | 38 | 34 | 10 | August | |||||||
| 38 | 38 | 36 | September | ||||||||
| 28 | 38 | 18 | 38 | 36 | October | ||||||
| 37 | 35 | 37 | November | ||||||||
| 25 | 36 | 37 | 37 | 35 | December |
By Season-year
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Season |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 24 | 16 | 24 | 76 | 72 | 52 | 6 | 35 | 37 | Winter | ||
| 20 | 47 | 76 | 38 | 38 | 66 | 35 | 73 | Spring | |||
| 75 | 76 | 36 | 38 | 72 | 34 | 35 | Summer | ||||
| 53 | 36 | 37 | 35 | 38 | 55 | 37 | 75 | 71 | Fall |
By Month-region
| Sub_region | Zone | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BB | Inshore | 7 | 4 | 7 | 9 | 4 | 8 | 10 | 4 | 6 | 8 | 6 | 10 |
| BB | Mid channel | 7 | 3 | 6 | 9 | 4 | 5 | 10 | 4 | 6 | 7 | 6 | 8 |
| BB | Offshore | 7 | 4 | 7 | 9 | 4 | 8 | 10 | 4 | 6 | 8 | 6 | 9 |
| UK | Inshore | 10 | 4 | 9 | 13 | 6 | 8 | 13 | 6 | 7 | 9 | 9 | 11 |
| UK | Mid channel | 7 | 2 | 7 | 5 | 3 | 9 | 9 | 3 | 6 | 6 | 5 | 8 |
| UK | Offshore | 12 | 3 | 9 | 11 | 5 | 12 | 14 | 5 | 9 | 11 | 8 | 11 |
| UK | Oceanic | 3 | 2 | 4 | 3 | 1 | 5 | 4 | 1 | 3 | 4 | 2 | 4 |
| MK | Inshore | 9 | 6 | 9 | 12 | 8 | 15 | 12 | 8 | 9 | 13 | 9 | 14 |
| MK | Mid channel | 9 | 7 | 11 | 12 | 8 | 13 | 13 | 8 | 9 | 13 | 9 | 14 |
| MK | Offshore | 10 | 6 | 11 | 13 | 7 | 13 | 13 | 8 | 9 | 13 | 9 | 15 |
| MK | Oceanic | 6 | 4 | 7 | 6 | 4 | 8 | 8 | 6 | 6 | 8 | 5 | 10 |
| LK | Inshore | 11 | 8 | 12 | 12 | 7 | 16 | 9 | 10 | 9 | 15 | 9 | 15 |
| LK | Mid channel | 12 | 9 | 12 | 12 | 7 | 15 | 10 | 11 | 9 | 15 | 9 | 15 |
| LK | Offshore | 18 | 7 | 13 | 14 | 10 | 18 | 13 | 15 | 12 | 19 | 12 | 17 |
| LK | Oceanic | 7 | 6 | 8 | 8 | 4 | 10 | 5 | 8 | 6 | 9 | 5 | 9 |
Per station during all sampling time
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | SiteID | Sub_region | Zone |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 1 | BB | Inshore |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 3 | BB | Offshore |
| 1 | 3 | 1 | 5 | 6 | 5 | 4 | 2 | 3 | 4 | 4 | UK | Inshore | |
| 1 | 3 | 1 | 1 | 5 | 6 | 5 | 4 | 2 | 3 | 4 | 5 | UK | Inshore |
| 2 | 2 | 1 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 4 | 5.5 | UK | Mid channel |
| 2 | 3 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 5 | 6 | UK | Offshore | |
| 1 | 4 | 1 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 5 | 6.5 | UK | Oceanic |
| 2 | 4 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 5 | 10 | MK | Inshore |
| 1 | 3 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 | 13 | MK | Inshore |
| 2 | 5 | 1 | 5 | 6 | 3 | 6 | 4 | 4 | 6 | 7 | MK | Inshore | |
| 2 | 3 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 11 | MK | Mid channel |
| 1 | 2 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 14 | MK | Mid channel |
| 2 | 4 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 8 | MK | Mid channel |
| 1 | 4 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 12 | MK | Offshore |
| 2 | 5 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 9 | MK | Offshore | |
| 2 | 4 | 1 | 1 | 5 | 6 | 5 | 4 | 3 | 4 | 5 | 9.5 | MK | Oceanic |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 3 | 5 | 16 | LK | Inshore |
| 2 | 5 | 1 | 5 | 6 | 4 | 6 | 4 | 4 | 6 | 19 | LK | Inshore | |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 24 | LK | Inshore |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 7 | 4 | 4 | 5 | 17 | LK | Mid channel |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 20 | LK | Mid channel |
| 1 | 5 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 23 | LK | Mid channel |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 7 | 4 | 4 | 6 | 18 | LK | Offshore |
| 1 | 4 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 21LK | LK | Offshore | |
| 1 | 2 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 22 | LK | Offshore |
| 2 | 5 | 1 | 1 | 4 | 6 | 5 | 5 | 4 | 3 | 6 | 21.5 | LK | Oceanic |
| 1 | 5 | 1 | 1 | 4 | 6 | 5 | 6 | 5 | 3 | 6 | 22.5 | LK | Oceanic |
| 1 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 2 | BB | Mid channel | |
| 3 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 15 | MK | Offshore | |
| 2 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 | 15.5 | MK | Oceanic | |
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 7 | EK_IN | BB | Inshore | |||
| 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | EK_MID | BB | Mid channel | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | EK_OFF | BB | Offshore | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 5 | UK_IN | UK | Inshore | |||
| 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | UK_MID | UK | Mid channel | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | UK_OFF | UK | Offshore | |||
| 5 | 6 | 5 | 7 | 5 | 4 | 6 | MR | UK | Offshore | ||||
| 5 | 6 | 5 | 7 | 5 | 4 | 6 | WS | LK | Offshore |
Remove extreme events?
# Label and filter extreme events
FLK.data$Extreme<-as.factor(FLK.data$Extreme)
summary(FLK.data$Extreme)## ColdMortality LowSalinty Normal
## 24 28 1486 FLK.data_filtered<-FLK.data[FLK.data$Extreme=="Normal",]3. TA and DIC salinity normalization
Salinity standardizations
- Uses salinity Bottle first, if not available, uses salinity from CTD
#MeanSalinity<-mean(FLK.data[FLK.data$Zone=="Oceanic",]$BestSalinity)
MeanSalinity<-mean(FLK.data$BestSalinity)
sdSalinity<-sd(FLK.data$BestSalinity)
Sal<-paste("Mean salinity = ", round(MeanSalinity, digits=3),
"(", round(sdSalinity, digits=3), "sd)")
Sal## [1] "Mean salinity = 35.998 ( 0.796 sd)"# TA_Sal<- ggplot(FLK.data) + MyTheme+
# geom_point(aes (BestSalinity, TA_umol_kg, fill=Season),
# alpha=0.5, shape=21, size=1)+
# geom_smooth(aes(BestSalinity, TA_umol_kg),
# linetype=2, method = "lm", color="black")
#
# ggExtra::ggMarginal(
# p = TA_Sal,
# type = 'boxplot',
# margins = 'both',
# size = 5,
# colour = 'black',
# fill = 'gray'
# )
#
# TA_Sal + facet_wrap(~Zone)
# TA_Sal+ facet_grid(Zone~Season)
# #TA_Sal+ facet_grid(Zone~Precipitation)
#
# DIC_Sal<- ggplot(FLK.data) + MyTheme+
# geom_point(aes (BestSalinity, DIC_umol_kg, fill=Season),
# alpha=0.5, shape=21, size=1)+
# geom_smooth(aes(BestSalinity, DIC_umol_kg),
# linetype=2, method = "lm", color="black")
#
# ggExtra::ggMarginal(
# p = DIC_Sal,
# type = 'boxplot',
# margins = 'both',
# size = 5,
# colour = 'black',
# fill = 'gray'
# )
#
# DIC_Sal + facet_wrap(~Zone)
# DIC_Sal+ facet_grid(Zone~Season)- Calculate TA intercepts
Note: Use Model II Linear regressions!
“For Model II regressions, neither X nor Y is an INDEPENDENT variable but both are assumed to be DEPENDENT on some other parameter which is often unknown. Neither are”controlled”, both are measured, and both include some error. We do not seek an equation of how Y varies in response to a change in X, but rather we look for how they both co-vary in time or space in response to some other variable or process.”
- Use ordinary least squares (OLS)?
All data
##
## Model II regression
##
## Call: lmodel2(formula = TA_umol_kg ~ BestSalinity, data =
## FLK.data_filtered)
##
## n = 1483 r = -0.2150909 r-square = 0.04626411
## Parametric P-values: 2-tailed = 5.554604e-17 1-tailed = 2.777302e-17
## Angle between the two OLS regression lines = 2.960342 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 3029.034 -18.44009 -86.89591 NA
## 2 MA 16727.365 -398.53141 -89.85623 NA
## 3 SMA 5454.193 -85.73162 -89.33171 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 2875.203 3182.866 -22.70767 -14.17252
## 2 MA 14028.065 21052.254 -518.53531 -323.63321
## 3 SMA 5304.217 5611.820 -90.10534 -81.57020
##
## Eigenvalues: 3689.572 0.4787585
##
## H statistic used for computing C.I. of MA: 3.372132e-07
Oceanic
##
## Model II regression
##
## Call: lmodel2(formula = TA_umol_kg ~ BestSalinity, data =
## FLK.data_filtered[FLK.data_filtered$Zone == "Oceanic", ])
##
## n = 190 r = 0.3439909 r-square = 0.1183298
## Parametric P-values: 2-tailed = 1.1773e-06 1-tailed = 5.886499e-07
## Angle between the two OLS regression lines = 6.931929 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 2120.6326 7.23549 82.13114 NA
## 2 MA 185.1964 61.02501 89.06119 NA
## 3 SMA 1624.1409 21.03396 87.27808 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 2018.377 2222.888 4.393988 10.07699
## 2 MA -1234.572 804.417 43.815670 100.48313
## 3 SMA 1515.024 1719.508 18.383517 24.06652
##
## Eigenvalues: 156.5575 0.3118211
##
## H statistic used for computing C.I. of MA: 4.139149e-05
- Calculate DIC intercepts
All data
##
## Model II regression
##
## Call: lmodel2(formula = DIC_umol_kg ~ BestSalinity, data =
## FLK.data_filtered)
##
## n = 1476 r = -0.3875632 r-square = 0.1502053
## Parametric P-values: 2-tailed = 4.286177e-54 1-tailed = 2.143089e-54
## Angle between the two OLS regression lines = 1.282267 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 3404.831 -37.96124 -88.49103 NA
## 2 MA 11143.923 -252.70667 -89.77327 NA
## 3 SMA 5566.678 -97.94849 -89.41506 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 3238.543 3571.119 -42.57453 -33.34794
## 2 MA 10157.088 12403.788 -287.66556 -225.32383
## 3 SMA 5404.336 5736.847 -102.67036 -93.44378
##
## Eigenvalues: 4831.897 0.4279791
##
## H statistic used for computing C.I. of MA: 2.312566e-07
Oceanic
##
## Model II regression
##
## Call: lmodel2(formula = DIC_umol_kg ~ BestSalinity, data =
## FLK.data_filtered[FLK.data_filtered$Zone == "Oceanic", ])
##
## n = 188 r = 0.134705 r-square = 0.01814545
## Parametric P-values: 2-tailed = 0.06531828 1-tailed = 0.03265914
## Angle between the two OLS regression lines = 11.74937 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 1877.6544 4.716863 78.03022 NA
## 2 MA -7297.9453 259.739297 89.77941 NA
## 3 SMA 787.4959 35.016231 88.36418 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 1697.0471 2058.2617 -0.302165 9.73589
## 2 MA 147947.6176 -2480.2122 125.837421 -4055.08510
## 3 SMA 594.0374 955.2024 30.355074 40.39313
##
## Eigenvalues: 437.9011 0.3506482
##
## H statistic used for computing C.I. of MA: 1.678203e-05
- Add TA and DIC endmembers (S=0) to data frame
# 4. Add endmember intercepts
TA_SO<-subset(TA_coef, term=="(Intercept)")
TA_SO<-as.data.frame(dplyr::select(TA_SO, c(Region, estimate)))
names(TA_SO)[2] <- "TA_0"
DIC_SO<-subset(DIC_coef, term=="(Intercept)")
DIC_SO<-as.data.frame(dplyr::select(DIC_SO, c(Region, estimate)))
names(DIC_SO)[2] <- "DIC_0"
Intercepts<-join(TA_SO, DIC_SO, by="Region", type="full")
FLK.data<-join(FLK.data, Intercepts, by="Region", type="left")- Normalize with (1) Friss equation and (2) mean salinity
# 5. endmember and salinity (mean) normalization
FLK.data$nTA<-((
(FLK.data$TA_umol_kg-FLK.data$TA_0)/
FLK.data$BestSalinity)*MeanSalinity)+FLK.data$TA_0
FLK.data$nDIC<-((
(FLK.data$DIC_umol_kg-FLK.data$DIC_0)/
FLK.data$BestSalinity)*MeanSalinity)+FLK.data$DIC_0
# 2. TA and DIC 35 normalization
FLK.data$n35TA<-(FLK.data$TA_umol_kg/FLK.data$BestSalinity)*MeanSalinity
FLK.data$n35DIC<-(FLK.data$DIC_umol_kg/FLK.data$BestSalinity)*MeanSalinity
# FLK.data$n35TA<-(FLK.data$TA_umol_kg/FLK.data$BestSalinity)*35
# FLK.data$n35DIC<-(FLK.data$DIC_umol_kg/FLK.data$BestSalinity)*35
#write.csv(FLK.data, "FLK_results/FLK.data_filtered_normalized.csv", row.names = F)- TA parameters to nTA (S=0)
kable(as.data.frame(TA_coef[,1:8], format = "markdown"),
caption = "Table 1: TA parameters for salinity normalization with endmember S=0",
digits = 3)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 2120.633 | 51.836 | 40.910 | 0 | 0.118 | 0.114 |
| FL | BestSalinity | 7.235 | 1.440 | 5.023 | 0 | 0.118 | 0.114 |
#write.csv(TA_coef, "TA_coef.csv")# Plot
Normalization_plot<- ggplot(FLK.data) +
theme_bw() +
theme(legend.position="bottom",
plot.background=element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
legend.box.background = element_rect(),
legend.title = element_blank(),
panel.background =element_rect(fill = NA,
color = "black"))+
scale_x_continuous(limits = c(31.5, 39),
expand = c(0.05, 0.05),
breaks = seq(30, 40, 2),
name=expression(paste("Salinity")))- DIC parameters to nDIC (S=0)
kable(as.data.frame(DIC_coef[,1:8], format = "markdown"),
caption = "Table 2: DIC parameters for salinity normalization with endmember S0",
digits = 2) %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 1877.65 | 91.55 | 20.51 | 0.00 | 0.02 | 0.01 |
| FL | BestSalinity | 4.72 | 2.54 | 1.85 | 0.07 | 0.02 | 0.01 |
#write.csv(DIC_coef, "DIC_coef.csv")
Figure Supp 1: Overview of linear regressions of total alkalinity (TA, µmol kg−1) and dissolved inorganic carbon (DIC, µmol kg−1) as a function of salinity + Friss normalized values.
- Original data: Black line and circles
- Friis normalization S0 end-member: purple line and pointing up triangles
- Mean salinity normalization: purple line and pointing down triangles
TA vs Friss salinity normalized TA
Figure Supp X: Linear regressions of Friss normalized total alkalinity (nTA, µmol kg−1) as a function of measured Total Alcalinity (TA, µmol kg−1)
DIC vs Friss salinity normalized DIC
Figure Supp X: Linear regressions of Friss normalized Dissolved Inorganc Carbon (nDIC, µmol kg−1) as a function of measured Dissolved Inorganc Carbon (DIC, µmol kg−1)
TA vs Salinity normalized TA
Figure Supp X: Linear regressions of mean salinity normalized total alkalinity (n35TA, µmol kg−1) as a function of measured Total Alcalinity (TA, µmol kg−1)
DIC vs Salinity normalized DIC
Figure Supp X: Linear regressions of mean salinity normalized Dissolved Inorganc Carbon (n35DIC, µmol kg−1) as a function of measured Dissolved Inorganc Carbon (DIC, µmol kg−1)
Differences vs Salinity
Figure Supp X: Linear regressions of (A) TA-n35TA and (B) DIC-nDIC as a function of Salinity
4. Endmember comparissions
FLK.data_filtered<-FLK.data[FLK.data$Extreme=="Normal",]4.1 TA and DIC relation
No_ocean.data<-FLK.data[FLK.data$Zone!="Oceanic",]
TA_DIC<- ggplot(No_ocean.data, aes (DIC_umol_kg, TA_umol_kg)) +
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
scale_x_continuous(# limits = c(1600, 2600),
# expand = c(0, 0),
# breaks = seq(1500,2600,100),
expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
facet_grid(Sub_region~Zone)+ MyTheme+
theme(legend.position = "bottom")+
#Season_fill+ Season_colour+
guides(fill=guide_legend(override.aes=list(shape=21)))TA_DIC_Zone <- ddply (FLK.data[FLK.data$Extreme=="Normal", ], .(Zone),summarise,
TAmean = mean (TA_umol_kg, na.rm = T),
DICmean = mean (DIC_umol_kg, na.rm = T),
nTAmean = mean (n35TA, na.rm = T),
nDICmean = mean (n35DIC, na.rm = T),
TAsd = sd (TA_umol_kg, na.rm = T),
DICsd = sd (DIC_umol_kg, na.rm = T),
nTAsd = sd (n35TA, na.rm = T),
nDIsd = sd (n35DIC, na.rm = T))
TA_DIC_Ocean<-TA_DIC_Zone[TA_DIC_Zone$Zone=="Oceanic", ]
kable(as.data.frame(TA_DIC_Zone, format = "markdown"),
caption = "Mean TA/DIC by zone") %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | TAmean | DICmean | nTAmean | nDICmean | TAsd | DICsd | nTAsd | nDIsd |
|---|---|---|---|---|---|---|---|---|
| Inshore | 2342.749 | 2020.618 | 2338.706 | 2018.103 | 95.44205 | 110.31121 | 131.68054 | 146.51718 |
| Mid channel | 2365.490 | 2036.908 | 2363.258 | 2035.343 | 51.29212 | 59.06801 | 66.22381 | 77.00690 |
| Offshore | 2376.692 | 2047.068 | 2376.637 | 2046.982 | 20.01152 | 26.30078 | 39.47022 | 41.52785 |
| Oceanic | 2380.977 | 2047.365 | 2382.627 | 2048.922 | 12.51062 | 20.92593 | 38.18416 | 38.46281 |
Note: Use Model II Linear regressions!
“For Model II regressions, neither X nor Y is an INDEPENDENT variable but both are assumed to be DEPENDENT on some other parameter which is often unknown. Neither are”controlled”, both are measured, and both include some error. We do not seek an equation of how Y varies in response to a change in X, but rather we look for how they both co-vary in time or space in response to some other variable or process.”
Zone and region
# Individual LR for each Zone and Sub_region
TA_DIC_coef_zone_noExtremes <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_zone_noExtremes<-subset(TA_DIC_coef_zone_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_zone_noExtremes[,1:8],
format = "markdown"),
caption = "TA vs DIC equations by zone and region without extreme events",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|---|---|
| Inshore | BB | DIC_umol_kg | 0.64 | 0.04 | 15.06 | 0 | 0.74 |
| Inshore | UK | DIC_umol_kg | 0.57 | 0.03 | 18.59 | 0 | 0.78 |
| Inshore | MK | DIC_umol_kg | 0.79 | 0.04 | 22.52 | 0 | 0.81 |
| Inshore | LK | DIC_umol_kg | 0.85 | 0.04 | 22.46 | 0 | 0.80 |
| Mid channel | BB | DIC_umol_kg | 0.49 | 0.05 | 9.21 | 0 | 0.54 |
| Mid channel | UK | DIC_umol_kg | 0.32 | 0.06 | 5.51 | 0 | 0.31 |
| Mid channel | MK | DIC_umol_kg | 0.80 | 0.04 | 21.14 | 0 | 0.79 |
| Mid channel | LK | DIC_umol_kg | 0.80 | 0.04 | 21.50 | 0 | 0.79 |
| Offshore | BB | DIC_umol_kg | 0.50 | 0.07 | 7.22 | 0 | 0.40 |
| Offshore | UK | DIC_umol_kg | 0.44 | 0.05 | 8.79 | 0 | 0.42 |
| Offshore | MK | DIC_umol_kg | 0.70 | 0.04 | 17.75 | 0 | 0.72 |
| Offshore | LK | DIC_umol_kg | 0.50 | 0.04 | 12.24 | 0 | 0.49 |
#write.csv(TA_DIC_coef_zone_noExtremes, "FLK_results/Fig_2_TA_DIC_coef_slopes_Subregion.csv", row.names = FALSE)
# Annotations for plots
coef_zone_noExtremes<-dplyr::select(TA_DIC_coef_zone_noExtremes,
c("Zone", "Sub_region", "estimate", "r.squared"))
coef_zone_noExtremes$estimate<-round(coef_zone_noExtremes$estimate, digits=2)
coef_zone_noExtremes$r.squared<-round(coef_zone_noExtremes$r.squared, digits=2)Type II Linear regression
TA_DIC_coef_zone_noExtII <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region) %>%
do({model = lmodel2(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
#TA_DIC_coef_zone_noExtII
TA_DIC_coef_zone_noExtII<-subset(TA_DIC_coef_zone_noExtII, term!="Intercept")
kable(as.data.frame(TA_DIC_coef_zone_noExtII[,1:9],
format = "markdown"),
caption = "TA vs DIC equations without extreme events using type II lm",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | method | term | estimate | conf.low | conf.high | p.value | r.squared |
|---|---|---|---|---|---|---|---|---|
| Inshore | BB | MA | Slope | 0.71 | 0.62 | 0.81 | 0.74 | |
| Inshore | BB | OLS | Slope | 0.64 | 0.56 | 0.73 | 0.74 | |
| Inshore | BB | SMA | Slope | 0.75 | 0.67 | 0.84 | 0.74 | |
| Inshore | UK | MA | Slope | 0.61 | 0.54 | 0.67 | 0.78 | |
| Inshore | UK | OLS | Slope | 0.57 | 0.50 | 0.63 | 0.78 | |
| Inshore | UK | SMA | Slope | 0.64 | 0.58 | 0.70 | 0.78 | |
| Inshore | MK | MA | Slope | 0.86 | 0.79 | 0.94 | 0.81 | |
| Inshore | MK | OLS | Slope | 0.79 | 0.72 | 0.86 | 0.81 | |
| Inshore | MK | SMA | Slope | 0.88 | 0.81 | 0.95 | 0.81 | |
| Inshore | LK | MA | Slope | 0.95 | 0.87 | 1.03 | 0.80 | |
| Inshore | LK | OLS | Slope | 0.85 | 0.78 | 0.93 | 0.80 | |
| Inshore | LK | SMA | Slope | 0.95 | 0.88 | 1.03 | 0.80 | |
| Mid channel | BB | MA | Slope | 0.58 | 0.46 | 0.71 | 0.54 | |
| Mid channel | BB | OLS | Slope | 0.49 | 0.38 | 0.59 | 0.54 | |
| Mid channel | BB | SMA | Slope | 0.66 | 0.57 | 0.78 | 0.54 | |
| Mid channel | UK | MA | Slope | 0.40 | 0.26 | 0.55 | 0.31 | |
| Mid channel | UK | OLS | Slope | 0.32 | 0.20 | 0.43 | 0.31 | |
| Mid channel | UK | SMA | Slope | 0.57 | 0.47 | 0.70 | 0.31 | |
| Mid channel | MK | MA | Slope | 0.88 | 0.81 | 0.97 | 0.79 | |
| Mid channel | MK | OLS | Slope | 0.80 | 0.72 | 0.87 | 0.79 | |
| Mid channel | MK | SMA | Slope | 0.90 | 0.83 | 0.97 | 0.79 | |
| Mid channel | LK | MA | Slope | 0.89 | 0.81 | 0.97 | 0.79 | |
| Mid channel | LK | OLS | Slope | 0.80 | 0.72 | 0.87 | 0.79 | |
| Mid channel | LK | SMA | Slope | 0.90 | 0.83 | 0.97 | 0.79 | |
| Offshore | BB | MA | Slope | 0.69 | 0.51 | 0.90 | 0.40 | |
| Offshore | BB | OLS | Slope | 0.50 | 0.36 | 0.64 | 0.40 | |
| Offshore | BB | SMA | Slope | 0.79 | 0.66 | 0.94 | 0.40 | |
| Offshore | UK | MA | Slope | 0.56 | 0.44 | 0.69 | 0.42 | |
| Offshore | UK | OLS | Slope | 0.44 | 0.34 | 0.54 | 0.42 | |
| Offshore | UK | SMA | Slope | 0.68 | 0.58 | 0.78 | 0.42 | |
| Offshore | MK | MA | Slope | 0.79 | 0.71 | 0.88 | 0.72 | |
| Offshore | MK | OLS | Slope | 0.70 | 0.62 | 0.77 | 0.72 | |
| Offshore | MK | SMA | Slope | 0.82 | 0.75 | 0.90 | 0.72 | |
| Offshore | LK | MA | Slope | 0.63 | 0.53 | 0.73 | 0.49 | |
| Offshore | LK | OLS | Slope | 0.50 | 0.42 | 0.58 | 0.49 | |
| Offshore | LK | SMA | Slope | 0.72 | 0.64 | 0.80 | 0.49 |
# Annotations for plots
TA_DIC_coef_zone_noExtII<-dplyr::select(TA_DIC_coef_zone_noExtII,
c("Zone", "Sub_region","method", "estimate", "r.squared"))
TA_DIC_coef_zone_noExtII$estimate<-round(TA_DIC_coef_zone_noExtII$estimate, digits=2)
TA_DIC_coef_zone_noExtII$r.squared<-round(TA_DIC_coef_zone_noExtII$r.squared, digits=2)4.2 By Precipitation - Removing Extreme events
TA_DIC_Extreme_pre<-TA_DIC +
geom_abline(intercept = -1700, slope=2, color="grey")+
geom_hline (yintercept = TA_DIC_Ocean$TAmean, color="grey")+
scale_shape_manual(values=c(24, 25, 21))+
geom_point(data=No_ocean.data, aes (DIC_umol_kg, TA_umol_kg,
fill=Precipitation, shape=Extreme),
alpha=0.5, size=1)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg),
method = "lm", colour="black", se=F)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg, colour=Precipitation),
method = "lm", se=F, size=0.5)+
geom_text(data = coef_zone_noExtremes,
aes(x = TA_DIC_Ocean$DICmean, y = 2070,
label = paste("Slo =", estimate,
" r.sq=", r.squared)),
size=2.5)+
# geom_abline(slope = 0, intercept =TA_DIC_Ocean$TAmean,
# linetype=2, colour="gray")+
# geom_vline(xintercept = TA_DIC_Ocean$DICmean,
# linetype=2, colour="gray")+
geom_point(aes(x=TA_DIC_Ocean$DICmean, y=TA_DIC_Ocean$TAmean),
size=3, alpha=1, shape=21, fill="black")
#TA_DIC_Extreme_preZone, region and transect
TA_DIC_reef_pre<-TA_DIC_Extreme_pre +
facet_grid(Zone~Reference)
#TA_DIC_reef_pre# Individual LR for each reef
TA_DIC_coef_Reef_noExtremes <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region, Reference, Precipitation) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_Reef_noExtremes<-subset(TA_DIC_coef_Reef_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_Reef_noExtremes[,1:11],
format = "markdown"),
caption = "TA vs DIC equations by reef without extreme events per trsansec",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | Reference | Precipitation | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|---|---|---|
| Inshore | BB | BB_1 | Dry | DIC_umol_kg | 0.55 | 0.06 | 8.67 | 0.00 | 0.79 | 0.78 |
| Inshore | BB | BB_1 | Wet | DIC_umol_kg | 0.72 | 0.06 | 11.10 | 0.00 | 0.87 | 0.86 |
| Inshore | BB | BB_2 | Dry | DIC_umol_kg | 0.52 | 0.07 | 7.40 | 0.00 | 0.75 | 0.74 |
| Inshore | BB | BB_2 | Wet | DIC_umol_kg | 0.47 | 0.12 | 4.00 | 0.00 | 0.50 | 0.47 |
| Inshore | UK | UK_3 | Dry | DIC_umol_kg | 0.56 | 0.03 | 17.91 | 0.00 | 0.90 | 0.90 |
| Inshore | UK | UK_3 | Wet | DIC_umol_kg | 0.47 | 0.09 | 5.16 | 0.00 | 0.50 | 0.48 |
| Inshore | UK | UK_4 | Dry | DIC_umol_kg | 0.53 | 0.05 | 10.75 | 0.00 | 0.88 | 0.87 |
| Inshore | UK | UK_4 | Wet | DIC_umol_kg | 0.32 | 0.15 | 2.20 | 0.04 | 0.23 | 0.18 |
| Inshore | MK | MK_5 | Dry | DIC_umol_kg | 0.55 | 0.07 | 8.14 | 0.00 | 0.79 | 0.77 |
| Inshore | MK | MK_5 | Wet | DIC_umol_kg | 0.66 | 0.09 | 6.99 | 0.00 | 0.73 | 0.72 |
| Inshore | MK | MK_6 | Dry | DIC_umol_kg | 0.70 | 0.09 | 7.50 | 0.00 | 0.77 | 0.75 |
| Inshore | MK | MK_6 | Wet | DIC_umol_kg | 0.79 | 0.11 | 7.11 | 0.00 | 0.73 | 0.71 |
| Inshore | MK | MK_7 | Dry | DIC_umol_kg | 0.66 | 0.08 | 8.47 | 0.00 | 0.82 | 0.81 |
| Inshore | MK | MK_7 | Wet | DIC_umol_kg | 0.87 | 0.13 | 6.93 | 0.00 | 0.72 | 0.70 |
| Inshore | LK | LK_8 | Dry | DIC_umol_kg | 0.77 | 0.10 | 8.04 | 0.00 | 0.77 | 0.76 |
| Inshore | LK | LK_8 | Wet | DIC_umol_kg | 0.83 | 0.09 | 8.77 | 0.00 | 0.81 | 0.80 |
| Inshore | LK | LK_9 | Dry | DIC_umol_kg | 0.66 | 0.09 | 7.26 | 0.00 | 0.75 | 0.73 |
| Inshore | LK | LK_9 | Wet | DIC_umol_kg | 0.75 | 0.14 | 5.35 | 0.00 | 0.61 | 0.59 |
| Inshore | LK | LK_10 | Dry | DIC_umol_kg | 0.83 | 0.14 | 5.90 | 0.00 | 0.62 | 0.61 |
| Inshore | LK | LK_10 | Wet | DIC_umol_kg | 0.71 | 0.12 | 5.84 | 0.00 | 0.64 | 0.62 |
| Mid channel | BB | BB_1 | Dry | DIC_umol_kg | 0.49 | 0.07 | 6.53 | 0.00 | 0.70 | 0.69 |
| Mid channel | BB | BB_1 | Wet | DIC_umol_kg | 0.56 | 0.12 | 4.76 | 0.00 | 0.57 | 0.55 |
| Mid channel | BB | BB_2 | Dry | DIC_umol_kg | 0.44 | 0.09 | 4.84 | 0.00 | 0.58 | 0.55 |
| Mid channel | BB | BB_2 | Wet | DIC_umol_kg | 0.26 | 0.13 | 1.96 | 0.07 | 0.20 | 0.15 |
| Mid channel | UK | UK_3 | Dry | DIC_umol_kg | 0.25 | 0.11 | 2.21 | 0.05 | 0.27 | 0.22 |
| Mid channel | UK | UK_3 | Wet | DIC_umol_kg | 0.03 | 0.13 | 0.22 | 0.83 | 0.00 | -0.06 |
| Mid channel | UK | UK_4 | Dry | DIC_umol_kg | 0.44 | 0.10 | 4.54 | 0.00 | 0.56 | 0.54 |
| Mid channel | UK | UK_4 | Wet | DIC_umol_kg | 0.26 | 0.13 | 1.97 | 0.07 | 0.20 | 0.15 |
| Mid channel | MK | MK_5 | Dry | DIC_umol_kg | 0.41 | 0.10 | 4.11 | 0.00 | 0.47 | 0.44 |
| Mid channel | MK | MK_5 | Wet | DIC_umol_kg | 0.80 | 0.15 | 5.32 | 0.00 | 0.60 | 0.58 |
| Mid channel | MK | MK_6 | Dry | DIC_umol_kg | 0.58 | 0.09 | 6.14 | 0.00 | 0.68 | 0.66 |
| Mid channel | MK | MK_6 | Wet | DIC_umol_kg | 0.80 | 0.09 | 8.92 | 0.00 | 0.82 | 0.81 |
| Mid channel | MK | MK_7 | Dry | DIC_umol_kg | 0.58 | 0.09 | 6.74 | 0.00 | 0.73 | 0.71 |
| Mid channel | MK | MK_7 | Wet | DIC_umol_kg | 1.01 | 0.11 | 9.64 | 0.00 | 0.84 | 0.83 |
| Mid channel | LK | LK_8 | Dry | DIC_umol_kg | 0.55 | 0.15 | 3.57 | 0.00 | 0.39 | 0.36 |
| Mid channel | LK | LK_8 | Wet | DIC_umol_kg | 0.95 | 0.06 | 14.84 | 0.00 | 0.92 | 0.92 |
| Mid channel | LK | LK_9 | Dry | DIC_umol_kg | 0.65 | 0.10 | 6.36 | 0.00 | 0.68 | 0.66 |
| Mid channel | LK | LK_9 | Wet | DIC_umol_kg | 0.84 | 0.11 | 7.80 | 0.00 | 0.76 | 0.75 |
| Mid channel | LK | LK_10 | Dry | DIC_umol_kg | 0.41 | 0.16 | 2.52 | 0.02 | 0.23 | 0.20 |
| Mid channel | LK | LK_10 | Wet | DIC_umol_kg | 0.83 | 0.11 | 7.68 | 0.00 | 0.77 | 0.75 |
| Offshore | BB | BB_1 | Dry | DIC_umol_kg | 0.45 | 0.09 | 4.95 | 0.00 | 0.55 | 0.53 |
| Offshore | BB | BB_1 | Wet | DIC_umol_kg | 0.54 | 0.17 | 3.23 | 0.00 | 0.35 | 0.32 |
| Offshore | BB | BB_2 | Dry | DIC_umol_kg | 0.41 | 0.11 | 3.63 | 0.00 | 0.44 | 0.40 |
| Offshore | BB | BB_2 | Wet | DIC_umol_kg | 0.50 | 0.22 | 2.24 | 0.04 | 0.24 | 0.19 |
| Offshore | UK | UK_3 | Dry | DIC_umol_kg | 0.41 | 0.09 | 4.34 | 0.00 | 0.38 | 0.36 |
| Offshore | UK | UK_3 | Wet | DIC_umol_kg | 0.42 | 0.11 | 3.64 | 0.00 | 0.27 | 0.25 |
| Offshore | UK | UK_4 | Dry | DIC_umol_kg | 0.35 | 0.09 | 3.85 | 0.00 | 0.47 | 0.43 |
| Offshore | UK | UK_4 | Wet | DIC_umol_kg | 0.64 | 0.14 | 4.61 | 0.00 | 0.57 | 0.54 |
| Offshore | MK | MK_5 | Dry | DIC_umol_kg | 0.42 | 0.09 | 4.74 | 0.00 | 0.54 | 0.52 |
| Offshore | MK | MK_5 | Wet | DIC_umol_kg | 0.78 | 0.08 | 9.57 | 0.00 | 0.83 | 0.82 |
| Offshore | MK | MK_6 | Dry | DIC_umol_kg | 0.42 | 0.09 | 4.51 | 0.00 | 0.50 | 0.48 |
| Offshore | MK | MK_6 | Wet | DIC_umol_kg | 0.95 | 0.12 | 7.89 | 0.00 | 0.79 | 0.77 |
| Offshore | MK | MK_7 | Dry | DIC_umol_kg | 0.36 | 0.08 | 4.43 | 0.00 | 0.54 | 0.51 |
| Offshore | MK | MK_7 | Wet | DIC_umol_kg | 0.81 | 0.10 | 7.85 | 0.00 | 0.77 | 0.76 |
| Offshore | LK | LK_8 | Dry | DIC_umol_kg | 0.24 | 0.08 | 3.20 | 0.00 | 0.35 | 0.32 |
| Offshore | LK | LK_8 | Wet | DIC_umol_kg | 0.88 | 0.19 | 4.58 | 0.00 | 0.51 | 0.49 |
| Offshore | LK | LK_9 | Dry | DIC_umol_kg | 0.47 | 0.08 | 5.95 | 0.00 | 0.69 | 0.67 |
| Offshore | LK | LK_9 | Wet | DIC_umol_kg | 0.66 | 0.11 | 5.92 | 0.00 | 0.66 | 0.64 |
| Offshore | LK | LK_10 | Dry | DIC_umol_kg | 0.35 | 0.08 | 4.41 | 0.00 | 0.35 | 0.33 |
| Offshore | LK | LK_10 | Wet | DIC_umol_kg | 0.56 | 0.09 | 5.97 | 0.00 | 0.49 | 0.48 |
#write.csv (TA_DIC_coef_Reef_noExtremes, "FLK_results/Fig_2_TA_DIC_slopes_Reefs.csv", row.names = FALSE)
TA_DIC_coef_Reef_noExtremes <-
No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Region, Sub_region, Transect, Precipitation) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model infoSlopes_reef<-subset(TA_DIC_coef_Reef_noExtremes, term!="(Intercept)")
Slope_plot<-ggplot(data=Slopes_reef, aes (x =Zone, y =estimate)) +
MyTheme+
scale_shape_manual(values=c(22, 23, 25))+
geom_boxplot(aes(fill=(Precipitation)), alpha=0.5,
position=position_dodge(width=0.3))+
geom_point(aes(fill = factor(Precipitation)),
position=position_dodge(width=0.3),
shape = 21, size=0.5)+
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA vs DIC slope")))+
theme(axis.text.x = element_text(angle = 0, vjust = 0.1),
legend.position = "bottom")+
#guides(fill=FALSE)+
facet_grid(Sub_region~Region)
# Season_fill+ Season_colour+
# theme(legend.title = element_blank())
#Slope_plot
Figure S9: TA (µmol kg−1) vs DIC (µmol kg−1) slopes per season and reef
4.3 By Season - Removing Extreme events
TA_DIC_season<-TA_DIC +
Season_colour+ Season_fill+
geom_abline(intercept = -1700, slope=2, color="grey")+
geom_hline (yintercept = TA_DIC_Ocean$TAmean, color="grey")+
scale_shape_manual(values=c(24, 25, 21))+
geom_point(data=No_ocean.data, aes (DIC_umol_kg, TA_umol_kg,
fill=Season, shape=Extreme),
alpha=0.5, size=1)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg),
method = "lm", colour="black", se=F)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg, colour=Season),
method = "lm", se=F, size=0.5)+
geom_text(data = coef_zone_noExtremes,
aes(x = TA_DIC_Ocean$DICmean, y = 2070,
label = paste("Slo =", estimate,
" r.sq=", r.squared)),
size=2.5)+
geom_point(aes(x=TA_DIC_Ocean$DICmean, y=TA_DIC_Ocean$TAmean),
size=3, alpha=1, shape=21, fill="black")
#TA_DIC_season# Individual LR for each reef
TA_DIC_coef_Season_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region, Season) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_Season_noExtremes<-subset(TA_DIC_coef_Season_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_Season_noExtremes[,1:11],
format = "markdown"),
caption = "TA vs DIC equations by season without extreme events",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | Season | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared | sigma |
|---|---|---|---|---|---|---|---|---|---|---|
| Inshore | BB | Winter | DIC_umol_kg | 0.67 | 0.10 | 6.59 | 0.00 | 0.74 | 0.73 | 23.70 |
| Inshore | BB | Spring | DIC_umol_kg | 0.86 | 0.11 | 7.52 | 0.00 | 0.75 | 0.74 | 36.03 |
| Inshore | BB | Summer | DIC_umol_kg | 0.53 | 0.11 | 4.95 | 0.00 | 0.58 | 0.55 | 33.48 |
| Inshore | BB | Fall | DIC_umol_kg | 0.78 | 0.09 | 8.29 | 0.00 | 0.77 | 0.75 | 23.40 |
| Inshore | UK | Winter | DIC_umol_kg | 0.71 | 0.05 | 13.83 | 0.00 | 0.91 | 0.90 | 21.12 |
| Inshore | UK | Spring | DIC_umol_kg | 0.58 | 0.06 | 8.96 | 0.00 | 0.76 | 0.75 | 22.93 |
| Inshore | UK | Summer | DIC_umol_kg | 0.57 | 0.19 | 2.98 | 0.01 | 0.27 | 0.24 | 42.13 |
| Inshore | UK | Fall | DIC_umol_kg | 0.71 | 0.06 | 11.46 | 0.00 | 0.84 | 0.83 | 26.86 |
| Inshore | MK | Winter | DIC_umol_kg | 0.84 | 0.08 | 10.16 | 0.00 | 0.84 | 0.83 | 34.06 |
| Inshore | MK | Spring | DIC_umol_kg | 0.84 | 0.10 | 8.32 | 0.00 | 0.68 | 0.67 | 41.83 |
| Inshore | MK | Summer | DIC_umol_kg | 0.95 | 0.07 | 13.67 | 0.00 | 0.87 | 0.87 | 37.15 |
| Inshore | MK | Fall | DIC_umol_kg | 0.79 | 0.07 | 10.76 | 0.00 | 0.79 | 0.78 | 28.77 |
| Inshore | LK | Winter | DIC_umol_kg | 0.64 | 0.12 | 5.22 | 0.00 | 0.51 | 0.49 | 44.09 |
| Inshore | LK | Spring | DIC_umol_kg | 0.87 | 0.08 | 10.52 | 0.00 | 0.77 | 0.76 | 35.49 |
| Inshore | LK | Summer | DIC_umol_kg | 0.97 | 0.08 | 12.26 | 0.00 | 0.86 | 0.86 | 36.38 |
| Inshore | LK | Fall | DIC_umol_kg | 1.04 | 0.06 | 17.57 | 0.00 | 0.90 | 0.90 | 26.09 |
| Mid channel | BB | Winter | DIC_umol_kg | 0.80 | 0.12 | 6.54 | 0.00 | 0.75 | 0.74 | 9.85 |
| Mid channel | BB | Spring | DIC_umol_kg | 0.64 | 0.08 | 7.88 | 0.00 | 0.80 | 0.78 | 11.95 |
| Mid channel | BB | Summer | DIC_umol_kg | 0.39 | 0.14 | 2.82 | 0.01 | 0.31 | 0.27 | 19.66 |
| Mid channel | BB | Fall | DIC_umol_kg | 0.48 | 0.09 | 5.22 | 0.00 | 0.59 | 0.57 | 11.79 |
| Mid channel | UK | Winter | DIC_umol_kg | 0.66 | 0.09 | 7.62 | 0.00 | 0.82 | 0.80 | 13.29 |
| Mid channel | UK | Spring | DIC_umol_kg | 0.01 | 0.13 | 0.04 | 0.97 | 0.00 | -0.07 | 13.59 |
| Mid channel | UK | Summer | DIC_umol_kg | 0.26 | 0.12 | 2.11 | 0.05 | 0.22 | 0.17 | 20.02 |
| Mid channel | UK | Fall | DIC_umol_kg | 0.43 | 0.11 | 4.02 | 0.00 | 0.49 | 0.46 | 14.77 |
| Mid channel | MK | Winter | DIC_umol_kg | 0.72 | 0.07 | 10.08 | 0.00 | 0.82 | 0.81 | 21.50 |
| Mid channel | MK | Spring | DIC_umol_kg | 0.97 | 0.08 | 11.47 | 0.00 | 0.81 | 0.80 | 29.81 |
| Mid channel | MK | Summer | DIC_umol_kg | 1.02 | 0.08 | 12.57 | 0.00 | 0.85 | 0.84 | 24.56 |
| Mid channel | MK | Fall | DIC_umol_kg | 0.57 | 0.06 | 8.91 | 0.00 | 0.72 | 0.71 | 16.21 |
| Mid channel | LK | Winter | DIC_umol_kg | 0.73 | 0.14 | 5.10 | 0.00 | 0.48 | 0.46 | 35.75 |
| Mid channel | LK | Spring | DIC_umol_kg | 0.74 | 0.08 | 9.40 | 0.00 | 0.73 | 0.73 | 27.01 |
| Mid channel | LK | Summer | DIC_umol_kg | 0.92 | 0.05 | 18.78 | 0.00 | 0.93 | 0.93 | 17.99 |
| Mid channel | LK | Fall | DIC_umol_kg | 0.62 | 0.13 | 4.84 | 0.00 | 0.41 | 0.39 | 19.83 |
| Offshore | BB | Winter | DIC_umol_kg | 0.74 | 0.12 | 5.98 | 0.00 | 0.70 | 0.68 | 8.49 |
| Offshore | BB | Spring | DIC_umol_kg | 0.48 | 0.13 | 3.63 | 0.00 | 0.41 | 0.38 | 8.38 |
| Offshore | BB | Summer | DIC_umol_kg | 0.59 | 0.21 | 2.84 | 0.01 | 0.31 | 0.27 | 20.07 |
| Offshore | BB | Fall | DIC_umol_kg | 0.34 | 0.07 | 4.65 | 0.00 | 0.52 | 0.50 | 8.52 |
| Offshore | UK | Winter | DIC_umol_kg | 0.63 | 0.08 | 8.01 | 0.00 | 0.75 | 0.74 | 6.58 |
| Offshore | UK | Spring | DIC_umol_kg | 0.48 | 0.17 | 2.86 | 0.01 | 0.25 | 0.22 | 10.70 |
| Offshore | UK | Summer | DIC_umol_kg | 0.48 | 0.11 | 4.52 | 0.00 | 0.44 | 0.42 | 14.09 |
| Offshore | UK | Fall | DIC_umol_kg | 0.29 | 0.08 | 3.42 | 0.00 | 0.30 | 0.28 | 11.49 |
| Offshore | MK | Winter | DIC_umol_kg | 0.62 | 0.09 | 6.95 | 0.00 | 0.68 | 0.66 | 8.56 |
| Offshore | MK | Spring | DIC_umol_kg | 0.89 | 0.07 | 12.65 | 0.00 | 0.84 | 0.83 | 13.80 |
| Offshore | MK | Summer | DIC_umol_kg | 0.75 | 0.08 | 9.42 | 0.00 | 0.76 | 0.75 | 16.30 |
| Offshore | MK | Fall | DIC_umol_kg | 0.34 | 0.06 | 5.43 | 0.00 | 0.48 | 0.46 | 9.89 |
| Offshore | LK | Winter | DIC_umol_kg | 0.48 | 0.09 | 5.28 | 0.00 | 0.47 | 0.45 | 10.09 |
| Offshore | LK | Spring | DIC_umol_kg | 0.61 | 0.08 | 7.90 | 0.00 | 0.61 | 0.60 | 11.79 |
| Offshore | LK | Summer | DIC_umol_kg | 0.60 | 0.11 | 5.36 | 0.00 | 0.45 | 0.43 | 17.60 |
| Offshore | LK | Fall | DIC_umol_kg | 0.24 | 0.05 | 4.84 | 0.00 | 0.35 | 0.34 | 7.77 |
| Oceanic | UK | Winter | DIC_umol_kg | 0.74 | 0.10 | 7.49 | 0.00 | 0.90 | 0.89 | 4.81 |
| Oceanic | UK | Spring | DIC_umol_kg | 0.17 | 0.21 | 0.82 | 0.44 | 0.09 | -0.04 | 5.84 |
| Oceanic | UK | Summer | DIC_umol_kg | 0.39 | 0.26 | 1.51 | 0.18 | 0.28 | 0.15 | 18.65 |
| Oceanic | UK | Fall | DIC_umol_kg | 0.24 | 0.14 | 1.76 | 0.12 | 0.31 | 0.21 | 10.38 |
| Oceanic | MK | Winter | DIC_umol_kg | 0.58 | 0.07 | 8.35 | 0.00 | 0.83 | 0.82 | 5.13 |
| Oceanic | MK | Spring | DIC_umol_kg | 0.45 | 0.12 | 3.77 | 0.00 | 0.47 | 0.44 | 8.16 |
| Oceanic | MK | Summer | DIC_umol_kg | 0.45 | 0.12 | 3.91 | 0.00 | 0.46 | 0.43 | 12.37 |
| Oceanic | MK | Fall | DIC_umol_kg | 0.30 | 0.09 | 3.12 | 0.01 | 0.34 | 0.30 | 9.95 |
| Oceanic | LK | Winter | DIC_umol_kg | 0.45 | 0.15 | 3.02 | 0.01 | 0.35 | 0.31 | 11.24 |
| Oceanic | LK | Spring | DIC_umol_kg | 0.51 | 0.11 | 4.73 | 0.00 | 0.53 | 0.50 | 8.38 |
| Oceanic | LK | Summer | DIC_umol_kg | 0.43 | 0.09 | 4.62 | 0.00 | 0.59 | 0.56 | 9.31 |
| Oceanic | LK | Fall | DIC_umol_kg | 0.24 | 0.07 | 3.54 | 0.00 | 0.40 | 0.37 | 6.98 |
#write.csv (TA_DIC_coef_Season_noExtremes, "FLK_results/Fig_5_TA_DIC_slopes_Reefs.csv", row.names = FALSE)
TA_DIC_coef_Season_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>%
group_by(Zone, Region, Sub_region, Season, Transect) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model infoSlopes_season<-subset(TA_DIC_coef_Season_noExtremes, term!="(Intercept)")
Slope_plot<-ggplot(data=Slopes_season, aes (x =Zone, y =estimate)) +
MyTheme+
Zone_shapes4+
geom_boxplot(aes(fill=Season), alpha=0.5,
position=position_dodge(width=0.3))+
geom_point(aes(fill = factor(Season)),
position=position_dodge(width=0.3),
shape = 21, size=0.5)+
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA vs DIC slope")))+
facet_grid(Sub_region~Region)+
Season_fill+
Season_colour+
guides(fill=FALSE)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.1),
legend.position = "bottom")
#Slope_plot
Figure 6: TA (µmol kg−1) vs DIC (µmol kg−1) slopes per season and reef
5 Seasonal plots
Data for endmember comparisons
# Reef
WS.reef<-subset(FLK.data, FLK.data$Zone!="Oceanic")
WS.reef$Date<-as.Date(WS.reef$Date, "%Y-%m-%d")
# Open
WS.open<-subset(FLK.data_filtered,
FLK.data_filtered$Zone=="Oceanic")
WS.open$Date<-as.Date(WS.open$Date, "%Y-%m-%d")
#summary(WS.open)# Mean ocean values by month - year
mean_Open_MY<- ddply (WS.open, .(MY, Month),
summarise,
OTem_my = mean (Temperature_C, na.rm = T),
OSal_my = mean (BestSalinity, na.rm = T),
OTA_my = mean (TA_umol_kg, na.rm = T),
OnTA_my = mean (n35TA, na.rm = T),
ODIC_my = mean (DIC_umol_kg, na.rm = T),
OnDIC_my = mean (n35DIC, na.rm = T),
OpCO2_O = mean (pCO2_uatm , na.rm = T),
OpH_O = mean (pH_calculated, na.rm = T),
OAra_O = mean (Aragonite_Sat_W , na.rm = T)
)
# Mean ocean values by month
mean_OpM <- mean_Open_MY %>%
group_by(Month) %>%
summarise(
OTem_m = mean (OTem_my, na.rm = T),
OSal_m = mean (OSal_my, na.rm = T),
OTA_m = mean (OTA_my, na.rm = T),
OnTA_m = mean (OnTA_my, na.rm = T),
OnDIC_m = mean (OnDIC_my, na.rm = T),
ODIC_m = mean (ODIC_my, na.rm = T),
OpCO2_m = mean (OpCO2_O , na.rm = T),
OpH_m = mean (OpH_O, na.rm = T),
OAra_m = mean (OAra_O, na.rm = T),
n = n()
)
#mean_OpM
# Mean ocean values by year
mean_Op<- mean_OpM %>%
summarise(
OTem = mean (OTem_m, na.rm = T),
OSal = mean (OSal_m, na.rm = T),
OTA = mean (OTA_m, na.rm = T),
OnTA = mean (OnTA_m, na.rm = T),
OnDIC = mean (OnDIC_m, na.rm = T),
ODIC = mean (ODIC_m, na.rm = T),
OpCO2 = mean (OpCO2_m, na.rm = T),
OpH = mean (OpH_m, na.rm = T),
OAra = mean (OAra_m, na.rm = T),
n = n()
)5.1 Month raw values
- Months stats
Month_Stats<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region),
summarise,
Tem_mean = mean (Temperature_C, na.rm = T),
Tem_sd = sd (Temperature_C, na.rm = T),
#Tem_se = std.error (Temperature_C, na.rm = T),
Sal_mean = mean (BestSalinity, na.rm = T),
Sal_sd = sd (BestSalinity, na.rm = T),
#Sal_se = std.error (BestSalinity, na.rm = T),
TA_mean = mean (TA_umol_kg, na.rm = T),
TA_sd = sd (TA_umol_kg, na.rm = T),
#TA_se = std.error (TA_umol_kg, na.rm = T),
nTA_mean = mean (n35TA, na.rm = T),
nTA_sd = sd (n35TA, na.rm = T),
DIC_mean = mean (DIC_umol_kg, na.rm = T),
DIC_sd = sd (DIC_umol_kg, na.rm = T),
#DIC_se = std.error (DIC_umol_kg, na.rm = T),
nDIC_mean = mean (n35DIC, na.rm = T),
nDIC_sd = sd (n35DIC, na.rm = T),
pCO2_mean = mean (pCO2_uatm , na.rm = T),
pCO2_sd = sd (pCO2_uatm , na.rm = T),
#pCO2_se = std.error (pCO2_uatm, na.rm = T),
pH_mean = mean (pH_calculated, na.rm = T),
pH_sd = sd (pH_calculated, na.rm = T),
#pH_se = std.error (pH_calculated, na.rm = T),
Ara_mean = mean (Aragonite_Sat_W , na.rm = T),
Ara_sd = sd (Aragonite_Sat_W , na.rm = T),
#Ara_se = std.error (Aragonite_Sat_W, na.rm = T),
n = n())
Month_Stats_2d<-Month_Stats %>%
mutate_if(is.numeric, round, digits=3)
Month_Stats_2d#write.csv(Month_Stats, "FLK_results/Table_S7_Month_Stats.csv", row.names = F)Temp
Month_Temp<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (Temperature_C, na.rm = T),
sd = sd (Temperature_C, na.rm = T))
Month_Temp<-Month_Temp %>%
mutate_if(is.numeric, round, digits=2)
Month_Temp$Region<-NULL
Month_Temp<-reshape(Month_Temp, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")Sal
Month_Sal<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (BestSalinity, na.rm = T),
sd = sd (BestSalinity, na.rm = T))
Month_Sal<-Month_Sal %>%
mutate_if(is.numeric, round, digits=2)
Month_Sal$Region<-NULL
Month_Sal<-reshape(Month_Sal, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")TA
Month_TA<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (TA_umol_kg, na.rm = T),
sd = sd (TA_umol_kg, na.rm = T))
Month_TA<-Month_TA %>%
mutate_if(is.numeric, round, digits=2)
Month_TA$Region<-NULL
Month_TA<-reshape(Month_TA, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")DIC
Month_DIC<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (DIC_umol_kg, na.rm = T),
sd = sd (DIC_umol_kg, na.rm = T))
Month_DIC<-Month_DIC %>%
mutate_if(is.numeric, round, digits=2)
Month_DIC$Region<-NULL
Month_DIC<-reshape(Month_DIC, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")pCO2
Month_pCO2<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (pCO2_uatm , na.rm = T),
sd = sd (pCO2_uatm , na.rm = T))
Month_pCO2<-Month_pCO2 %>%
mutate_if(is.numeric, round, digits=2)
Month_pCO2$Region<-NULL
Month_pCO2<-reshape(Month_pCO2, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")pH
Month_pH<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (pH_calculated, na.rm = T),
sd = sd (pH_calculated, na.rm = T))
Month_pH<-Month_pH %>%
mutate_if(is.numeric, round, digits=2)
Month_pH$Region<-NULL
Month_pH<-reshape(Month_pH, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")Arag
Month_Ara<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (Aragonite_Sat_W , na.rm = T),
sd = sd (Aragonite_Sat_W , na.rm = T))
Month_Ara<-Month_Ara %>%
mutate_if(is.numeric, round, digits=2)
Month_Ara$Region<-NULL
Month_Ara<-reshape(Month_Ara, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")all
Month_Stats3<-rbind(Month_Temp, Month_Sal, Month_TA, Month_DIC,
Month_pCO2, Month_pH, Month_Ara)
#write.csv(Month_Stats3, "FLK_results/Table_S7_Month_Stats_wide.csv", row.names = F)Seasonal range
Seasonal_range<-ddply (Month_Stats, .(Zone, Sub_region),
summarise,
Tem_meany = mean (Tem_mean, na.rm = T),
Tem_sdy = sd (Tem_mean, na.rm = T),
Tem_max = max (Tem_mean, na.rm = T),
Tem_min = min (Tem_mean, na.rm = T),
Tem_diff = (max (Tem_mean, na.rm = T) - min (Tem_mean, na.rm = T)),
Sal_meany = mean (Sal_mean, na.rm = T),
Sal_sdy = sd (Sal_mean, na.rm = T),
Sal_max = max (Sal_mean, na.rm = T),
Sal_min = min (Sal_mean, na.rm = T),
Sal_diff = (max (Sal_mean, na.rm = T) - min (Sal_mean, na.rm = T)),
TA_meany = mean (TA_mean, na.rm = T),
TA_sdy = sd (TA_mean, na.rm = T),
TA_max = max (TA_mean, na.rm = T),
TA_min = min (TA_mean, na.rm = T),
TA_diff = (max (TA_mean, na.rm = T) - min (TA_mean, na.rm = T)),
DIC_meany = mean (DIC_mean, na.rm = T),
DIC_sdy = sd (DIC_mean, na.rm = T),
DIC_max = max (DIC_mean, na.rm = T),
DIC_min = min (DIC_mean, na.rm = T),
DIC_diff = (max (DIC_mean, na.rm = T) - min (DIC_mean, na.rm = T)),
pCO2_meany = mean (pCO2_mean, na.rm = T),
pCO2_sdy = sd (pCO2_mean, na.rm = T),
pCO2_max = max (pCO2_mean, na.rm = T),
pCO2_min = min (pCO2_mean, na.rm = T),
pCO2_dif = (max (pCO2_mean, na.rm = T) - min (pCO2_mean, na.rm = T)),
pH_meany = mean (pH_mean, na.rm = T),
pH_sdy = sd (pH_mean, na.rm = T),
pH_max = max (pH_mean, na.rm = T),
pH_min = min (pH_mean, na.rm = T),
pH_diff = (max (pH_mean, na.rm = T) - min (pH_mean, na.rm = T)),
Arg_meany = mean (Ara_mean, na.rm = T),
Arg_sdy = sd (Ara_mean, na.rm = T),
Ara_max = max (Ara_mean, na.rm = T),
Ara_min = min (Ara_mean, na.rm = T),
Ara_diff = (max (Ara_mean, na.rm = T) - min (Ara_mean, na.rm = T)),
nTA_max = max (nTA_mean, na.rm = T),
nTA_min = min (nTA_mean, na.rm = T),
nTA_diff = (max (nTA_mean, na.rm = T) - min (nTA_mean, na.rm = T)),
nDIC_max = max (nDIC_mean, na.rm = T),
nDIC_min = min (nDIC_mean, na.rm = T),
nDIC_diff = (max (nDIC_mean, na.rm = T) - min (nDIC_mean, na.rm = T)),
n = n())
Seasonal_range<-Seasonal_range %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_range#Seasonal_range$pH_max_diff<-Seasonal_range$pH_max-mean()
#write.csv(Seasonal_range, "FLK_results/Table_S8_Month_mean_range.csv", row.names = F)- Month Plots
Month_ploth<- ggplot(FLK.data) +
MyTheme +
facet_grid(Sub_region~Zone)+
scale_shape_manual(values=c(24, 25, 21))+
guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank() )
Year_ploth<- ggplot(Month_Stats) +
MyTheme +
theme(axis.title.x = element_blank(), axis.text.x=element_text(angle = 10, vjust = 0.5))+
Season_fill+
Season_colour+
facet_grid(Sub_region~Region)
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
#theme(axis.title.x = element_blank() )
Month_Season_plotTA_DIC<- ggplot(FLK.data_filtered[
FLK.data_filtered$Zone!="Oceanic",]) +
MyTheme +
facet_grid(~Zone)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(#strip.text = element_blank(),
legend.position = "none",
axis.title.x = element_blank()
)
Month_Season_plot<- ggplot(FLK.data_filtered) +
MyTheme +
facet_grid(~Zone)+
Zone_shapes4+
#Region_fill+
#Region_colour+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(axis.title.x = element_blank())#,
# legend.position = "none")
Year_plot_2<- ggplot(Month_Stats)+
#[Month_Stats$Zone!="Oceanic",]) +
MyTheme +
facet_grid(~Zone)+
Zone_shapes4+
guides(fill=guide_legend(override.aes=list(shape=21)))+
#Season_fill+
#Season_colour+
theme(#axis.title.x = element_blank()#,
#axis.text.y=element_blank(),
legend.position = "bottom"
)
Year_plot_3<- ggplot(Month_Stats, aes(fill=Sub_region, shape=Zone))+
#[Month_Stats$Zone!="Oceanic",]) +
MyTheme +
Zone_shapes4+
facet_grid(~Region)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(#axis.title.x = element_blank()#,
#axis.text.y=element_blank(),
legend.position = "right",
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 20, vjust = 0.5)
)Temperature
Figure Supp 2: Temperature variation by the region and shelf position by (A) month of the year and (B) season.
Temp_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OTem, color="grey")+
scale_y_continuous(#limits = c(15,40),
name=(expression("Temperature"~(degree*C))),
breaks = seq(16, 40, 2),
expand = c(0.05, 0.05)) +
geom_smooth(aes (x=as.numeric(Month2),
y=Temperature_C, colour=Sub_region),
span=0.6, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=Temperature_C, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=Temperature_C, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Temp_expSalinity
Figure Supp 3: Salinity variation by the region and shelf position by (A) month of the year and (B) season.
Sal_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OSal, color="grey")+
scale_y_continuous(#limits = c(15,40),
name=(expression("Salinity")),
breaks = seq(15, 40, 1),
expand = c(0.05, 0.05)) +
geom_smooth(aes (x=as.numeric(Month2),
y=BestSalinity, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=BestSalinity, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=BestSalinity, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Sal_expTA
Figure Supp XX: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3A
TA_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(#limits = c(2100,2500),
expand = c(0.05, 0.05),
breaks = seq(2100, 2500, 100),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=TA_umol_kg, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=TA_umol_kg, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=TA_umol_kg, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#
TA_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(#limits = c(2150,2550),
expand = c(0.05, 0.05),
breaks = seq(2200, 2500, 50),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
stat_summary(aes (x=Sub_region, y=TA_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2)+
stat_summary(aes (x=Sub_region, y=TA_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#TA_Y2
TA_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(limits = c(2130,2510),
expand = c(0.05, 0.05),
breaks = seq(2100, 2500, 100),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
geom_point(data=Seasonal_range, aes (x=Zone, y=TA_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=TA_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = TA_min,
# xend = Zone, yend = TA_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=TA_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=TA_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#TA_Y3
Figure 3A: Total Alkalinity (TA) variation by the region and shelf position. Left: monthly mean values, Right: Yearly mean values
nTA
DIC
Figure Supp XX: Disolved Inorganic Carbon (DIC) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3B
DIC_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(#limits = c(1780,2270),
expand = c(0.05, 0.05),
breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=DIC_umol_kg, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=DIC_umol_kg, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width=0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=DIC_umol_kg, fill=Sub_region, shape=Zone),
size=1,
fun = "mean", geom = "point",
position=position_dodge(width=0.3))
#DIC_exp
DIC_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
#breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
stat_summary(aes (x=Sub_region, y=DIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)+
stat_summary(aes (x=Sub_region, y=DIC_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#DIC_Y2
DIC_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(limits = c(1820,2230),
expand = c(0.00, 0.00),
breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
geom_point(data=Seasonal_range, aes (x=Zone, y=DIC_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=DIC_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = DIC_min,
# xend = Zone, yend = DIC_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=DIC_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=DIC_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#DIC_Y3
Figure 3B: Dissolved Inorganic Carbon (DIC) by the region and shelf position. Left: monthly mean values, Right: Yearly mean values
nDIC
pCO2
Figure Supp 6: pCO2 variation by the region and shelf position by (A) month of the year and (B) season.
pCO2_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OpCO2, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=expression(paste(pCO[2], " (", mu, " atm)")),
breaks = seq(200, 600, 100),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=pCO2_uatm, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=pCO2_uatm, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=pCO2_uatm, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pCO2_exp
pCO2_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OpCO2, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=expression(paste(pCO[2],
" (", mu, " atm)")),
breaks = seq(200, 600, 50),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=pCO2_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)+
stat_summary(aes (x=Sub_region, y=pCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)
#pCO2_Y2
pH
Figure Supp 7: pH variation by the region and shelf position by (A) month of the year and (B) season.
pH_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(#limits = c(7.88,8.25),
name=(expression (pH)),
breaks = seq(0, 9, 0.1),
expand = c(0.02, 0)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=pH_calculated, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=pH_calculated, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=pH_calculated, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pH_exp
pH_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=(expression (pH[calculated])),
breaks = seq(0, 9, 0.05),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=pH_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)+
stat_summary(aes (x=Sub_region, y=pH_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)
#pH_Y2
pH_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(limits = c(7.87,8.22),
name=(expression (pH)),
breaks = seq(0, 9, 0.1),
expand = c(0.02, 0)
)+
geom_point(data=Seasonal_range, aes (x=Zone, y=pH_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=pH_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = pH_min,
# xend = Zone, yend = pH_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=pH_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=pH_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#pH_Y3Figure 3C
Omega
Figure Supp 8: Omega variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3D
Omega_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OAra, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=(expression("\n" ~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, colour=Sub_region),
span=0.8, alpha=0.2, se=F
#method = lm, formula = y ~ splines::bs(x, 3),
)+
stat_summary(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Omega_exp
Om_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OAra, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
# name=(expression (
# "Aragonite saturation state "(~Omega[Ar]))),
name=(expression (
~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=Ara_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)+
stat_summary(aes (x=Sub_region, y=Ara_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#Om_Y2
Om_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OAra , color="grey")+
scale_y_continuous(#limits = c(1,5.5),
# name=(expression (
# "Aragonite saturation state "(~Omega[Ar]))),
name=(expression (
~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.1, 0.1)
) +
geom_point(data=Seasonal_range, aes (x=Zone, y=Ara_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=Ara_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = Ara_min,
# xend = Zone, yend = Ara_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=Ara_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=Ara_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#Om_Y3
Figure 3C: Aragonite saturation state (omega) by the region and shelf position. Left: monthly mean values, Right: Yearly mean values + SD, maximum monthly mean and minimum monthly mean.
Fig.3: Seasonal monthly plot
No summary
with summary
Fig.3: Seasonal monthly plot supplemental
Figure 3: Monthly seasonal patterns in (A) Total Alkalinity (TA), (B) Dissolved Inorganic Carbon (DIC), and (C) aragonite saturation state (Ωar) in the Florida Reef Tract. The colors represent the biogeographical regions and the vertical panels separate the locations by shelf zone. The points show the (monthly mean values ± 95 CI) and the lines the smoothed curves for the data (method = ‘loess’ and formula ‘y ~ x’, span=0.8). Gray horizontal lines in each panel demarcate the mean values of the parameters in the oceanic samples.
5.2 Season raw values
- Season stats (all data)
- do not use for further calculations since some months have more data than
- can be used to compare the aggregated meas ussing MM
Seasonal_Stats<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season),
summarise,
Tem_mean = mean (Temperature_C, na.rm = T),
Tem_sd = sd (Temperature_C, na.rm = T),
Sal_mean = mean (BestSalinity, na.rm = T),
Sal_sd = sd (BestSalinity, na.rm = T),
TA_mean = mean (TA_umol_kg, na.rm = T),
TA_sd = sd (TA_umol_kg, na.rm = T),
nTA_mean = mean (n35TA, na.rm = T),
nTA_sd = sd (n35TA, na.rm = T),
nDIC_mean = mean (n35DIC, na.rm = T),
nDIC_sd = sd (n35DIC, na.rm = T),
DIC_mean = mean (DIC_umol_kg, na.rm = T),
DIC_sd = sd (DIC_umol_kg, na.rm = T),
pCO2_mean = mean (pCO2_uatm , na.rm = T),
pCO2_sd = sd (pCO2_uatm , na.rm = T),
pH_mean = mean (pH_calculated, na.rm = T),
pH_sd = sd (pH_calculated, na.rm = T),
Ara_mean = mean (Aragonite_Sat_W , na.rm = T),
Ara_sd = sd (Aragonite_Sat_W , na.rm = T),
n())
Seasonal_Stats<-Seasonal_Stats %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_Stats- Season stats (monthly mean)
Seasonal_Stats2<- ddply (Month_Stats, .(Zone, Sub_region, Season),
summarise,
Tem_M = mean (Tem_mean, na.rm = T),
#Tem_sd = sd (Tem_mean, na.rm = T),
Tem_se = std.error (Tem_mean, na.rm = T),
Sal_M = mean (Sal_mean, na.rm = T),
#Sal_sd = sd (Sal_mean, na.rm = T),
Sal_se = std.error (Sal_mean, na.rm = T),
TA_M = mean (TA_mean, na.rm = T),
#TA_sd = sd (TA_mean, na.rm = T),
TA_se = std.error (TA_mean, na.rm = T),
DIC_M = mean (DIC_mean, na.rm = T),
#DIC_sd = sd (DIC_mean, na.rm = T),
DIC_se = std.error (DIC_mean, na.rm = T),
#nTA_M = mean (nTA_mean, na.rm = T),
#nTA_sd = sd (nTA_mean, na.rm = T),
#nDIC_M = mean (nDIC_mean, na.rm = T),
#nDIC_sd = sd (nDIC_mean, na.rm = T),
pCO2_M = mean (pCO2_mean , na.rm = T),
#pCO2_sd = sd (pCO2_mean , na.rm = T),
pCO2_se = std.error (pCO2_mean , na.rm = T),
pH_M = mean (pH_mean, na.rm = T),
#pH_sd = sd (pH_mean, na.rm = T),
pH_se = std.error (pH_mean, na.rm = T),
Ara_M = mean (Ara_mean , na.rm = T),
#Ara_sd = sd (Ara_mean , na.rm = T),
Ara_se = std.error (Ara_mean , na.rm = T),
n())
Seasonal_Stats2<-Seasonal_Stats2 %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_Stats2# write.csv(Seasonal_Stats2, "FLK_results/Table_S_Season_A.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
- Yearly stats (monthly mean)
Yearly_Stats<- ddply (Month_Stats, .(Zone, Sub_region),
summarise,
Tem_Y = mean (Tem_mean, na.rm = T),
Tem_sd = sd (Tem_mean, na.rm = T),
Sal_Y = mean (Sal_mean, na.rm = T),
Sal_sd = sd (Sal_mean, na.rm = T),
TA_Y = mean (TA_mean, na.rm = T),
TA_sd = sd (TA_mean, na.rm = T),
nTA_Y = mean (nTA_mean, na.rm = T),
nTA_sd = sd (nTA_mean, na.rm = T),
nDIC_Y = mean (nDIC_mean, na.rm = T),
nDIC_sd = sd (nDIC_mean, na.rm = T),
DIC_Y = mean (DIC_mean, na.rm = T),
DIC_sd = sd (DIC_mean, na.rm = T),
pCO2_Y = mean (pCO2_mean , na.rm = T),
pCO2_sd = sd (pCO2_mean , na.rm = T),
pH_Y = mean (pH_mean, na.rm = T),
pH_sd = sd (pH_mean, na.rm = T),
Ara_Y = mean (Ara_mean , na.rm = T),
Ara_sd = sd (Ara_mean , na.rm = T),
n())
Yearly_Stats<-Yearly_Stats %>%
mutate_if(is.numeric, round, digits=2)
Yearly_Stats# write.csv(Yearly_Stats, "FLK_results/Table_1_Year_A.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
5.3 Delta months values
# 1. Get the oceanic mean values per month-year
WS.reef<-join(WS.reef, mean_Open_MY,
type = "left", by=c("MY", "Month"))
# 2. and subtract them from the reef data
# Delta TA
WS.reef$dTA_MY<-WS.reef$TA_umol_kg-WS.reef$OTA_my
# Delta DIC
WS.reef$dDIC_MY<-WS.reef$DIC_umol_kg-WS.reef$ODIC_my
# Delta nTA
WS.reef$dnTA_MY<-WS.reef$n35TA-WS.reef$OnTA_my
# Delta nDIC
WS.reef$dnDIC_MY<-WS.reef$n35DIC-WS.reef$OnDIC_my
# Delta pCO2
WS.reef$dpCO2_MY<-WS.reef$pCO2_uatm-WS.reef$OpCO2_O
# Delta pH
WS.reef$dpH_MY<-WS.reef$pH_calculated-WS.reef$OpH_O
# Delta Omega
WS.reef$dOme_MY<-WS.reef$Aragonite_Sat_W-WS.reef$OAra_O
# head(WS.reef)
#summary(WS.reef$Season, digits=2)- Delta month stats
dMonth_Stats<- ddply (WS.reef, .(Zone, Sub_region, Month2, Season, Region),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
#dTA_se = std.error (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dDIC_se = std.error (dDIC_MY, na.rm = T),
dnDIC_mean = mean (dnDIC_MY, na.rm = T),
dnDIC_sd = sd (dnDIC_MY, na.rm = T),
dpCO2_mean = mean (dpCO2_MY , na.rm = T),
dpCO2_sd = sd (dpCO2_MY , na.rm = T),
#dpCO2_se = std.error (dpCO2_MY, na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = sd (dpH_MY, na.rm = T),
#dPH_se = std.error (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = sd (dOme_MY , na.rm = T)#,
#dAra_se = std.error (dOme_MY, na.rm = T),
)
dMonth_Stats<-dMonth_Stats %>%
mutate_if(is.numeric, round, digits=3)
dMonth_Stats#write.csv(dMonth_Stats, "FLK_results/Table_S9_Month_StatsDeltas.csv", row.names = F)
Spring.data<-WS.reef[WS.reef$Season=="Spring", ]
Summer.data<-WS.reef[WS.reef$Season=="Summer", ]
Fall.data<-WS.reef[WS.reef$Season=="Fall", ]
Winter.data<-WS.reef[WS.reef$Season=="Winter", ]Seasonal range
Seasonal_range_d<-ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
dTA_max = max (dTA_mean, na.rm = T),
dTA_min = min (dTA_mean, na.rm = T),
dTA_diff = (max (dTA_mean, na.rm = T) - min (dTA_mean, na.rm = T)),
dDIC_max = max (dDIC_mean, na.rm = T),
dDIC_min = min (dDIC_mean, na.rm = T),
dDIC_diff = (max (dDIC_mean, na.rm = T) - min (dDIC_mean, na.rm = T)),
dpCO2_max = max (dpCO2_mean, na.rm = T),
dpCO2_min = min (dpCO2_mean, na.rm = T),
dpCO2_dif = (max (dpCO2_mean, na.rm = T) - min (dpCO2_mean, na.rm = T)),
dpH_max = max (dpH_mean, na.rm = T),
dpH_min = min (dpH_mean, na.rm = T),
dpH_diff = (max (dpH_mean, na.rm = T) - min (dpH_mean, na.rm = T)),
dAra_max = max (dAra_mean, na.rm = T),
dAra_min = min (dAra_mean, na.rm = T),
dAra_diff = (max (dAra_mean, na.rm = T) - min (dAra_mean, na.rm = T)),
n = n())
Seasonal_range_d<-Seasonal_range_d %>%
mutate_if(is.numeric, round, digits=3)
Seasonal_range_d#write.csv(Seasonal_range_d, "FLK_results/Table_S8_Month_mean_range_d.csv", row.names = F)- Delta month plots
Delta_plot<- ggplot(WS.reef) +
MyTheme +
facet_grid(Sub_region~Zone)+
scale_shape_manual(values=c(24, 25, 21))+
guides(fill=guide_legend(override.aes=list(shape=21)))+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank())
DeltaYear_plot<- ggplot(dMonth_Stats) +
MyTheme +
facet_grid(Sub_region~Region)+
Season_fill+
Season_colour+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 10,
vjust = 0.5))
Delta_plot_month<- ggplot(WS.reef) +
MyTheme +
geom_abline(slope = 0, intercept = 0, color="grey")+
facet_grid(~Zone)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(axis.title.x = element_blank(),
legend.position = "bottom")
DeltaYear_plot2<- ggplot(dMonth_Stats) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))
DeltaYear_plot3<- ggplot(dMonth_Stats) +
MyTheme +
Zone_shapes3+
facet_grid(~Region)+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))
Frequency_plot<- ggplot(data = WS.reef) +
geom_vline(aes(xintercept = 0), linetype = "dashed")+
facet_grid(Zone~Sub_region, scales = "free_y")+ MyThemedTA
Figure Supp 4: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 4A
DTA_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
breaks = seq(-800, 800, 100),
name=expression(paste(Delta,
"TA (", mu, "mol ", ~kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dTA_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dTA_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dTA_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#DTA_exp
TA_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "TA (", mu, "mol ", ~kg^-1, ")")))+
stat_summary(aes (x=Season, y=dTA_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dTA_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#TA_delta_Y2
dTA_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-210,70),
expand = c(0.03, 0.03),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Zone, y=dTA_mean,
fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5,
position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dTA_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))+
facet_grid(~Region)
#dTA_Y3
Figure 4A: Seasonal delta Total Alkalinity (dTA) variation by region and shelf position. Mean values and 95 CI
dDIC
Figure Supp 5: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 4B
dDIC_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
breaks = seq(-800, 800, 100),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dDIC_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dDIC_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dDIC_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#dDIC_exp
DIC_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
stat_summary(aes (x=Season, y=dDIC_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dDIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#DIC_delta_Y2
dDIC_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-185,130),
expand = c(0.01, 0.01),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Zone, y=dDIC_mean,
fill=Sub_region, shape = Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5,
position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dDIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))
#dDIC_Y3
Figure 4B: Seasonal delta Dissolved Inorganic Carbon (dDIC) variation by region and shelf position.
dDIC density
dDIC_freq_All<- Frequency_plot+
geom_density(aes (dDIC_MY), fill = "lightgray", alpha=0.5) +
scale_x_continuous(breaks = seq(-300, 300, 100),
expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
#dDIC_freq_All
dDIC_freq_pre<- Frequency_plot+
geom_density(aes (dDIC_MY, fill = Precipitation),
alpha = 0.4) +
scale_x_continuous(breaks = seq(-300, 300, 100),
expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
#dDIC_freq_pre
# dDIC_freq_sea<- Frequency_plot+
# geom_density(aes (dDIC_MY, fill = Season), alpha = 0.4) +
# scale_x_continuous(breaks = seq(-300, 300, 100),
# expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
# dDIC_freq_seadpCO2
pdCO2_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
#breaks = seq(1800, 2800, 100),
name=expression(paste(Delta ~ pCO[2] , " (", mu, "atm)")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dpCO2_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dpCO2_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dpCO2_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pdCO2_exp
pCO2_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.1, 0.1),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta ~ pCO[2] , " (", mu, "atm)")))+
stat_summary(aes (x=Season, y=dpCO2_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dpCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#pCO2_delta_Y2
dPCO2_Y3<- DeltaYear_plot2+
scale_y_continuous(#limits = c(2150,2550),
expand = c(0.01, 0.01),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Sub_region, y=dpCO2_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "point",
size=1.5, shape=21)+
stat_summary(aes (x=Sub_region, y=dpCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2)
#dPCO2_Y3
dpH
Figure 4C
dOmega
Figure 4D
DOmega_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1,5.5),
name=(expression (paste(Delta ,
~Omega[Ar]))),
breaks = seq(-3, 3, 0.5),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=dOme_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dOme_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dOme_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))+
facet_grid(Zone~.)
#DOmega_exp
om_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
#breaks = seq(-800, 800, 50),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
stat_summary(aes (x=Season, y=dAra_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dAra_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))+
theme(panel.spacing = unit(0.8, "lines"))
#om_delta_Y2
dOm_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-0.9, 1),
expand = c(0.02, 0.02),
breaks = seq(-5, 5, 0.5),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
stat_summary(aes (x=Zone, y=dAra_mean,
fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5, position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dAra_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))
#dOm_Y3
- Figure 4: Seasonal Δ and yearly sums
Seasonality_deltas<-ggarrange(
TA_delta_Y2 +theme(legend.position = "none",
axis.text.x=element_blank()),
DIC_delta_Y2+theme(legend.position = "none",
strip.text.x = element_blank(),
axis.text.x=element_blank()),
pH_delta_Y2+theme(legend.position = "none",
strip.text.x = element_blank(),
axis.text.x=element_blank()),
om_delta_Y2+ theme(panel.spacing = unit(0.8, "lines"),
strip.text.x = element_blank()),
#TA_DIC_sum,
nrow = 4,
#labels = c("A", "B", " C"),
heights =c(1, 0.9, 0.9, 1.2))
#widths = c(1, 1, 0.9))
Seasonality_deltas
Seasonality_deltas3<-ggarrange(
dTA_my3,
dDIC_my3,
dpH_my3,
dOm_my3,
nrow = 4,
#labels = c("A", "B", " C"),
heights =c(1, 0.9, 0.9, 1))
Seasonality_deltas3
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_season.svg", plot=Seasonality_deltas, dpi = 300, width=6, height=6)
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_ICRS.svg", plot=Seasonality_deltas, dpi = 300, width=8, height=6)
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_Summary.svg", plot=Seasonality_deltas3, dpi = 300, width=8, height=8)Figure 4: Seasonal inshore-ocean gradients in the Florida Keys. (A) Seasonal ΔTA. (B) Seasonal ΔDIC. (C) Seasonal ΩAr. The bars represent the mean Δ values per season, region, and shelf position (± 95 CI).
5.4 Delta season values
- Delta season stats (all data)
dSeason_Stats<- ddply (WS.reef, .(Zone, Sub_region, Season),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dnDIC_mean = mean (dnDIC_MY, na.rm = T),
#dnDIC_sd = sd (dnDIC_MY, na.rm = T),
#dpCO2_mean = mean (dpCO2_MY , na.rm = T),
#dpCO2_sd = sd (dpCO2_MY , na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = mean (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = mean (dOme_MY , na.rm = T),
n())
dSeason_Stats<-dSeason_Stats %>%
mutate_if(is.numeric, round, digits=3)
dSeason_Stats- Delta season stats (monthly mean)
dSeasonal_Stats2<- ddply (dMonth_Stats,
.(Zone, Season, Sub_region),
summarise,
dTA_M = mean (dTA_mean, na.rm = T),
dTA_sd = sd (dTA_mean, na.rm = T),
dTA_se = std.error (dTA_mean, na.rm = T),
dDIC_M = mean (dDIC_mean, na.rm = T),
dDIC_sd = sd (dDIC_mean, na.rm = T),
dDIC_se = std.error (dDIC_mean, na.rm = T),
#dnTA_M = mean (dnTA_mean, na.rm = T),
#dnTA_sd = sd (dnTA_mean, na.rm = T),
#dnDIC_M = mean (dnDIC_mean, na.rm = T),
#dnDIC_sd = sd (dnDIC_mean, na.rm = T),
dpCO2_M = mean (dpCO2_mean , na.rm = T),
dpCO2_sd = sd (dpCO2_mean , na.rm = T),
dpCO2_se = std.error (dpCO2_mean , na.rm = T),
dpH_M = mean (dpH_mean, na.rm = T),
dpH_sd = sd (dpH_mean, na.rm = T),
dpH_se = std.error (dpH_mean, na.rm = T),
dAra_M = mean (dAra_mean , na.rm = T),
dAra_sd = sd (dAra_mean , na.rm = T),
dAra_se = std.error (dAra_mean , na.rm = T),
n())
dSeasonal_Stats2<-dSeasonal_Stats2 %>%
mutate_if(is.numeric, round, digits=3)
dSeasonal_Stats2#write.csv(dSeasonal_Stats2, "FLK_results/Table_2_SeasonDelta_B.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
- Delta yearly stats (monthly mean)
dYearly_Stats<- ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
dTA_M = mean (dTA_mean, na.rm = T),
dTA_sd = sd (dTA_mean, na.rm = T),
#dTA_se = std.error (dTA_mean, na.rm = T),
dDIC_M = mean (dDIC_mean, na.rm = T),
dDIC_sd = sd (dDIC_mean, na.rm = T),
#dDIC_se = std.error (dDIC_mean, na.rm = T),
dnTA_M = mean (dnTA_mean, na.rm = T),
dnTA_sd = sd (dnTA_mean, na.rm = T),
dnDIC_M = mean (dnDIC_mean, na.rm = T),
dnDIC_sd = sd (dnDIC_mean, na.rm = T),
dpCO2_M = mean (dpCO2_mean , na.rm = T),
dpCO2_sd = sd (dpCO2_mean , na.rm = T),
#dpCO2_se = std.error (dpCO2_mean , na.rm = T),
dpH_M = mean (dpH_mean, na.rm = T),
dpH_sd = sd (dpH_mean, na.rm = T),
#dpH_se = std.error (dpH_mean, na.rm = T),
dAra_M = mean (dAra_mean , na.rm = T),
dAra_sd = sd (dAra_mean , na.rm = T),
#dAra_se = std.error (dAra_mean , na.rm = T),
n())
dYearly_Stats<-dYearly_Stats %>%
mutate_if(is.numeric, round, digits=3)
dYearly_Stats#write.csv(dYearly_Stats, "FLK_results/Table_1b_delta_year.csv", row.names = F)- Yearly summ (monthly mean)
dYear_Add<- ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
su_dTA_Y = sum (dTA_mean, na.rm = T),
#m_TA_Y = mean (dTA_mean, na.rm = T),
#sd_TA_Y = sd (dTA_mean, na.rm = T),
su_dDIC_Y = sum (dDIC_mean, na.rm = T),
#m_dDIC_Y = mean (dDIC_mean, na.rm = T),
#sd_dDIC_Y = sd (dDIC_mean, na.rm = T),
su_dpCO2_Y = sum (dpCO2_mean, na.rm = T),
#m_pCO2_Y = mean (dpCO2_mean, na.rm = T),
#sd_pCO2_Y = sd (dpCO2_mean, na.rm = T),
su_dpH_Y = sum (dpH_mean, na.rm = T),
#m_pH_Y = mean (dpH_mean, na.rm = T),
#sd_pH_Y = sd (dpH_mean, na.rm = T),
su_dAra_Y = sum (dAra_mean, na.rm = T),
#m_Ara_Y = mean (dAra_mean, na.rm = T),
#sd_Ara_Y = sd (dAra_mean, na.rm = T),
n())
dYear_Add<-dYear_Add %>%
mutate_if(is.numeric, round, digits=2)
dYear_Add#write.csv(dYear_Add, "FLK_results/Table_1_year_byMM.csv", row.names = F)- Yearly summ (indiviual years)
dYear_Add2<- ddply (WS.reef, .(Zone, Sub_region, MY, Region, Year),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
#dTA_se = std.error (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dDIC_se = std.error (dDIC_MY, na.rm = T),
dnDIC_mean = mean (dnDIC_MY, na.rm = T),
dnDIC_sd = sd (dnDIC_MY, na.rm = T),
dpCO2_mean = mean (dpCO2_MY , na.rm = T),
dpCO2_sd = sd (dpCO2_MY , na.rm = T),
#dpCO2_se = std.error (dpCO2_MY, na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = mean (dpH_MY, na.rm = T),
#dPH_se = std.error (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = mean (dOme_MY , na.rm = T),
#dAra_se = std.error (dOme_MY, na.rm = T),
n())
dYear_Add2<-dYear_Add2 %>%
mutate_if(is.numeric, round, digits=3)
dYear_Add2dYear_Add2<-dYear_Add2[dYear_Add2$Year>2014, ]
dYear_Add2<-dYear_Add2[dYear_Add2$Year<2022, ]
dYear_Add3<- ddply (dYear_Add2, .(Zone, Sub_region, Year),
summarise,
su_dTA_Y = sum (dTA_mean, na.rm = T),
#m_TA_Y = mean (dTA_mean, na.rm = T),
#sd_TA_Y = sd (dTA_mean, na.rm = T),
su_dDIC_Y = sum (dDIC_mean, na.rm = T),
#m_dDIC_Y = mean (dDIC_mean, na.rm = T),
#sd_dDIC_Y = sd (dDIC_mean, na.rm = T),
su_dpCO2_Y = sum (dpCO2_mean, na.rm = T),
#m_pCO2_Y = mean (dpCO2_mean, na.rm = T),
#sd_pCO2_Y = sd (dpCO2_mean, na.rm = T),
su_dpH_Y = sum (dpH_mean, na.rm = T),
#m_pH_Y = mean (dpH_mean, na.rm = T),
#sd_pH_Y = sd (dpH_mean, na.rm = T),
su_dAra_Y = sum (dAra_mean, na.rm = T),
#m_Ara_Y = mean (dAra_mean, na.rm = T),
#sd_Ara_Y = sd (dAra_mean, na.rm = T),
n())
dYear_Add3<-dYear_Add3 %>%
mutate_if(is.numeric, round, digits=2)
dYear_Add35.5 Seasoal models
season.data<-WS.reef[WS.reef$Extreme=="Normal", ]
season.data<-droplevels(season.data)
season.data$Zone<-factor(season.data$Zone, levels = c("Offshore", "Mid channel", "Inshore"))
#summary(season.data)
inshore.season.data<-season.data[season.data$Zone=="Inshore", ]
midchanel.season.data<-season.data[season.data$Zone=="Mid channel", ]
ofshore.season.data<-season.data[season.data$Zone=="Offshore", ]LMER - TA month
hist(FLK.data_filtered$TA_umol_kg)
shapiro.test(FLK.data_filtered$TA_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: FLK.data_filtered$TA_umol_kg
## W = 0.81083, p-value < 2.2e-16#
# dTA_seson<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
TA_month_0<-lmer(TA_umol_kg~ Zone * Sub_region * Month +
(1|SiteID), data=FLK.data_filtered)
#anova(TA_month_0)
TA_month<-lmer(TA_umol_kg~ Zone + Sub_region + Month +
Zone:Sub_region + Zone:Month + Sub_region:Month +
(1|SiteID), data=season.data)
#summary(dTA_seson)
av.TA_month<-(anova(TA_month))
av.TA_month ranova(TA_month) #step(TA_month)
plot(TA_month)
par(mfrow=c(1,2))
qqnorm(resid(TA_month))
acf(resid(TA_month), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(TA_month, scale = TRUE)
#plot_summs(TA_month)
TA_mo.emmc<-emmeans(TA_month, ~Zone*Month, by="Sub_region")
TA_mon.emmc<-multcomp::cld(TA_mo.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
#TA_mon.emmc<-TA_mon.emmc[order(TA_mon.emmc$Zone, TA_mon.emmc$Month), ]
TA_mon.emmcLMER - TA season
hist(season.data$TA_umol_kg)
shapiro.test(season.data$TA_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$TA_umol_kg
## W = 0.84003, p-value < 2.2e-16#
# dTA_seson<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
TA_season_0<-lmer(TA_umol_kg~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
anova(TA_season_0) TA_season<-lmer(TA_umol_kg~ Zone + Sub_region + Season +
Zone:Sub_region + Zone:Season + Sub_region:Season +
(1|SiteID), data=season.data)
anova(TA_season_0, TA_season) #summary(av.TA_season)
av.TA_season<-(anova(TA_season))
av.TA_season ranova(TA_season) #step(TA_season)
plot(TA_season)
par(mfrow=c(1,2))
qqnorm(resid(TA_season))
acf(resid(TA_season), main="acf(resid(TA Season))")
par(mfrow=c(1,1))
#summ(av.TA_season, scale = TRUE)
#plot_summs(TA_season)
TA_se.emmc<-emmeans(TA_season, ~Zone*Season, by="Sub_region")
TA_seas.emmc<-multcomp::cld(TA_se.emmc)
TA_seas.emmc TA_se.emmc2<-emmeans(TA_season, ~Sub_region*Season, by="Zone")
TA_seas.emmc2<-multcomp::cld(TA_se.emmc2)
TA_seas.emmc2LMER - all dTA - MODEL 13
hist(season.data$dTA_MY)
shapiro.test(season.data$dTA_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dTA_MY
## W = 0.81295, p-value < 2.2e-16#
# dTA_seson<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dTA_seson<-lmer(dTA_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dTA_seson)
av.dTA_seson<-(anova(dTA_seson))
av.dTA_seson ranova(dTA_seson) #step(dTA_seson)
plot(dTA_seson)
par(mfrow=c(1,2))
qqnorm(resid(dTA_seson))
acf(resid(dTA_seson), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dTA_seson, scale = TRUE)
#plot_summs(dTA_seson) dTA_ses.emmc<-emmeans(dTA_seson, ~Season*Sub_region, by="Zone")
dTA_ses.emmc<-multcomp::cld(dTA_ses.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dTA_ses.emmc<-dTA_ses.emmc[order(dTA_ses.emmc$Zone,
dTA_ses.emmc$Season,
dTA_ses.emmc$Sub_region), ]
dTA_ses.emmc dTA_ses.emmc[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal dTA") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 1.05 | 11.86 | 242.13 | -22.33 | 24.42 | 1 |
| Winter | UK | 3.89 | 10.17 | 278.88 | -16.13 | 23.91 | 1 |
| Winter | MK | 4.60 | 9.78 | 249.88 | -14.66 | 23.85 | 1 |
| Winter | LK | 1.95 | 8.39 | 239.51 | -14.59 | 18.48 | 1 |
| Spring | BB | -3.90 | 11.28 | 199.06 | -26.15 | 18.35 | 1 |
| Spring | UK | -1.98 | 9.60 | 233.02 | -20.91 | 16.94 | 1 |
| Spring | MK | -7.46 | 8.86 | 176.87 | -24.94 | 10.02 | 1 |
| Spring | LK | -11.49 | 7.62 | 170.97 | -26.52 | 3.55 | 1 |
| Summer | BB | -4.71 | 11.01 | 186.94 | -26.43 | 17.01 | 1 |
| Summer | UK | -5.37 | 9.28 | 207.21 | -23.67 | 12.93 | 1 |
| Summer | MK | -21.53 | 8.99 | 186.94 | -39.26 | -3.79 | 1 |
| Summer | LK | -10.34 | 7.79 | 186.94 | -25.69 | 5.02 | 1 |
| Fall | BB | -0.04 | 10.55 | 158.72 | -20.87 | 20.79 | 1 |
| Fall | UK | -1.47 | 9.13 | 197.13 | -19.47 | 16.54 | 1 |
| Fall | MK | -1.23 | 8.49 | 151.90 | -18.01 | 15.55 | 1 |
| Fall | LK | -3.36 | 7.38 | 153.29 | -17.94 | 11.23 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 8.02 | 12.20 | 267.02 | -16.00 | 32.04 | 34 |
| Winter | UK | 10.66 | 12.57 | 293.82 | -14.09 | 35.40 | 34 |
| Winter | MK | 25.23 | 9.78 | 249.88 | 5.98 | 44.49 | 4 |
| Winter | LK | 13.54 | 8.99 | 185.97 | -4.20 | 31.28 | 4 |
| Spring | BB | -8.62 | 12.21 | 263.60 | -32.66 | 15.42 | 234 |
| Spring | UK | -3.02 | 11.86 | 242.08 | -26.40 | 20.35 | 234 |
| Spring | MK | -37.31 | 8.73 | 167.88 | -54.54 | -20.07 | 123 |
| Spring | LK | -49.12 | 8.49 | 151.91 | -65.90 | -32.33 | 12 |
| Summer | BB | -11.51 | 11.01 | 186.94 | -33.23 | 10.21 | 234 |
| Summer | UK | -7.34 | 11.56 | 219.31 | -30.13 | 15.44 | 234 |
| Summer | MK | -61.62 | 8.99 | 186.94 | -79.36 | -43.89 | 1 |
| Summer | LK | -67.34 | 8.99 | 186.94 | -85.07 | -49.60 | 1 |
| Fall | BB | 4.02 | 10.77 | 172.23 | -17.23 | 25.28 | 34 |
| Fall | UK | 0.48 | 11.27 | 202.70 | -21.74 | 22.71 | 34 |
| Fall | MK | 6.05 | 8.61 | 159.82 | -10.96 | 23.05 | 4 |
| Fall | LK | -11.15 | 8.28 | 138.33 | -27.52 | 5.22 | 234 |
| Inshore | |||||||
| Winter | BB | 40.60 | 11.86 | 242.13 | 17.23 | 63.97 | 56 |
| Winter | UK | 48.74 | 10.61 | 323.46 | 27.87 | 69.60 | 56 |
| Winter | MK | 57.00 | 10.38 | 304.17 | 36.58 | 77.41 | 6 |
| Winter | LK | 7.31 | 9.29 | 206.24 | -10.99 | 25.62 | 45 |
| Spring | BB | -62.58 | 11.28 | 199.10 | -84.83 | -40.32 | 23 |
| Spring | UK | -57.07 | 9.96 | 267.02 | -76.68 | -37.46 | 23 |
| Spring | MK | -80.92 | 8.49 | 151.90 | -97.70 | -64.14 | 2 |
| Spring | LK | -124.51 | 8.38 | 144.78 | -141.08 | -107.94 | 1 |
| Summer | BB | -37.62 | 11.01 | 186.94 | -59.34 | -15.90 | 234 |
| Summer | UK | -33.41 | 9.60 | 233.02 | -52.33 | -14.49 | 34 |
| Summer | MK | -122.12 | 9.13 | 197.16 | -140.13 | -104.11 | 1 |
| Summer | LK | -157.07 | 9.28 | 208.69 | -175.36 | -138.78 | 1 |
| Fall | BB | 23.38 | 10.34 | 148.24 | 2.96 | 43.80 | 56 |
| Fall | UK | 43.60 | 9.43 | 221.70 | 25.00 | 62.19 | 56 |
| Fall | MK | 14.54 | 8.61 | 159.09 | -2.46 | 31.55 | 56 |
| Fall | LK | -60.08 | 8.28 | 138.33 | -76.45 | -43.71 | 23 |
dTA_year.emmc<-emmeans(dTA_seson, ~Sub_region*Zone)
dTA_year.emmc<-multcomp::cld(dTA_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dTA_year.emmc<-dTA_year.emmc[order(dTA_year.emmc$Zone,
dTA_year.emmc$Sub_region), ]
#dTA_year.emmc
dTA_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year dTA") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | -1.90 | 6.27 | 21.01 | -14.94 | 11.14 | 34 |
| UK | -1.23 | 5.31 | 24.32 | -12.18 | 9.71 | 4 |
| MK | -6.40 | 5.08 | 20.44 | -16.98 | 4.17 | 34 |
| LK | -5.81 | 4.38 | 20.16 | -14.95 | 3.34 | 34 |
| Mid-Channel | ||||||
| BB | -2.02 | 6.44 | 23.46 | -15.32 | 11.28 | 34 |
| UK | 0.19 | 6.55 | 25.21 | -13.30 | 13.69 | 34 |
| MK | -16.91 | 5.07 | 20.41 | -27.48 | -6.34 | 234 |
| LK | -28.52 | 4.92 | 18.07 | -38.85 | -18.18 | 23 |
| Inshore | ||||||
| BB | -9.06 | 6.25 | 20.74 | -22.06 | 3.95 | 234 |
| UK | 0.46 | 5.47 | 27.42 | -10.75 | 11.67 | 4 |
| MK | -32.88 | 5.14 | 21.47 | -43.55 | -22.20 | 2 |
| LK | -83.59 | 4.98 | 18.86 | -94.01 | -73.16 | 1 |
Figure 4A: Model
dTA_ses_2.emmc<-as.data.frame(dTA_ses.emmc)
dTA_ye_3.emmc<-as.data.frame(dTA_year.emmc)
dTA_ye_3.emmc$Season<-"Mean"
dTA_all_4<-rbind(dTA_ses_2.emmc, dTA_ye_3.emmc)
dTA_all_4$Zone<-factor(dTA_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearTA<- ggplot(dTA_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "TA (", mu, "mol ", ~kg^-1, ")")))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearTA
LMER - dTA Inshore
# Inshore dTA
hist(inshore.season.data$dTA_MY)
shapiro.test(inshore.season.data$dTA_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dTA_MY
## W = 0.96669, p-value = 3.329e-08 dTA_seson_in<-lmer(dTA_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dTA_seson_in)
av.dTA_seson_in<-anova(dTA_seson_in)
av.dTA_seson_in ranova(dTA_seson_in) #step(dTA_seson_in)
plot(dTA_seson_in)
par(mfrow=c(1,2))
qqnorm(resid(dTA_seson_in))
acf(resid(dTA_seson_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dTA_seson_in, scale = TRUE)
#plot_summs(dTA_seson_in)
dTA_ses_in.emmc<-emmeans(dTA_seson_in, ~ Sub_region*Season)
dTA_ses_in.emmc<-multcomp::cld(dTA_ses_in.emmc)
#dTA_ses_in.emmc<-as.data.frame(dTA_ses_in.emmc[complete.cases(dTA_ses_in.emmc),])
dTA_ses_in.emmc<-dTA_ses_in.emmc[order(dTA_ses_in.emmc$Season,
dTA_ses_in.emmc$Sub_region), ]
dTA_ses_in.emmcLMER - DIC season
hist(season.data$DIC_umol_kg)
shapiro.test(season.data$DIC_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$DIC_umol_kg
## W = 0.92541, p-value < 2.2e-16 DIC_season_0<-lmer(DIC_umol_kg~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(DIC_season_0)
av.DIC_season<-(anova(DIC_season_0))
av.DIC_season ranova(DIC_season_0) #step(DIC_season_0)
plot(DIC_season_0)
par(mfrow=c(1,2))
qqnorm(resid(DIC_season_0))
acf(resid(DIC_season_0), main="acf(resid(DIC Season))")
par(mfrow=c(1,1))
#summ(av.TA_season, scale = TRUE)
#plot_summs(av.DIC_season)
DIC_se.emmc<-emmeans(DIC_season_0, ~Zone*Season, by="Sub_region")
DIC_seas.emmc<-multcomp::cld(DIC_se.emmc)
DIC_seas.emmc DIC_se.emmc2<-emmeans(DIC_season_0, ~Sub_region*Season, by="Zone")
DIC_seas.emmc2<-multcomp::cld(TA_se.emmc2)
DIC_seas.emmc2LMER - all dDIC - MODEL 14
# Inshore Omega
hist(season.data$dDIC_MY)
shapiro.test(season.data$dDIC_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dDIC_MY
## W = 0.87049, p-value < 2.2e-16 # dDIC_seson<-lmer(dDIC_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dDIC_seson<-lmer(dDIC_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dDIC_seson)
av.dDIC_seson<-anova(dDIC_seson)
av.dDIC_seson ranova(dDIC_seson) step(dDIC_seson)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 50 -6573.3 13247
## (1 | SiteID) 0 49 -6576.0 13250 5.426 1 0.01984 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Zone:Sub_region:Season 0 94802 5266.8 18 1208 2.2734 0.001778
##
## Zone:Sub_region:Season **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Zone * Sub_region * Season + (1 | SiteID) plot(dDIC_seson)
par(mfrow=c(1,2))
qqnorm(resid(dDIC_seson))
acf(resid(dDIC_seson), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_seson, scale = TRUE)
#plot_summs(dDIC_seson)
dDIC_ses.emmc<-emmeans(dDIC_seson, ~Season*Sub_region, by="Zone")
dDIC_ses.emmc<-multcomp::cld(dDIC_ses.emmc)
#dDIC_ses.emmc<-as.data.frame(dDIC_ses.emmc[complete.cases(dDIC_ses.emmc),])
dDIC_ses.emmc<-dDIC_ses.emmc[order(dDIC_ses.emmc$Zone,
dDIC_ses.emmc$Season,
dDIC_ses.emmc$Sub_region), ]
#dDIC_ses.emmc
dDIC_ses.emmc[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal dDIC") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | -1.15 | 12.72 | 167.12 | -26.27 | 23.96 | 1 |
| Winter | UK | -0.10 | 10.86 | 193.67 | -21.52 | 21.33 | 1 |
| Winter | MK | 7.93 | 10.47 | 172.29 | -12.74 | 28.61 | 1 |
| Winter | LK | 7.33 | 8.79 | 152.28 | -10.04 | 24.70 | 1 |
| Spring | BB | -0.53 | 12.16 | 139.83 | -24.57 | 23.51 | 1 |
| Spring | UK | -1.51 | 10.31 | 161.61 | -21.86 | 18.85 | 1 |
| Spring | MK | -13.60 | 9.58 | 123.97 | -32.56 | 5.36 | 1 |
| Spring | LK | -6.06 | 8.25 | 120.59 | -22.39 | 10.27 | 1 |
| Summer | BB | 4.53 | 11.89 | 130.44 | -18.99 | 28.05 | 1 |
| Summer | UK | -3.19 | 9.99 | 144.36 | -22.94 | 16.56 | 1 |
| Summer | MK | -17.56 | 9.71 | 130.44 | -36.77 | 1.64 | 1 |
| Summer | LK | 0.38 | 8.68 | 146.89 | -16.78 | 17.54 | 1 |
| Fall | BB | 3.67 | 11.44 | 112.44 | -19.00 | 26.34 | 1 |
| Fall | UK | 2.40 | 9.84 | 137.26 | -17.07 | 21.86 | 1 |
| Fall | MK | 0.53 | 9.23 | 107.70 | -17.76 | 18.82 | 1 |
| Fall | LK | 1.48 | 8.02 | 108.77 | -14.42 | 17.37 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 10.65 | 13.05 | 183.59 | -15.10 | 36.40 | 45 |
| Winter | UK | 1.73 | 13.42 | 202.15 | -24.73 | 28.19 | 2345 |
| Winter | MK | 24.47 | 10.47 | 172.29 | 3.79 | 45.14 | 45 |
| Winter | LK | 31.80 | 9.71 | 130.16 | 12.59 | 51.01 | 5 |
| Spring | BB | -21.87 | 13.06 | 182.44 | -47.64 | 3.90 | 12345 |
| Spring | UK | -29.06 | 12.72 | 167.09 | -54.17 | -3.94 | 1234 |
| Spring | MK | -65.70 | 9.46 | 118.11 | -84.43 | -46.98 | 1 |
| Spring | LK | -53.49 | 9.23 | 107.70 | -71.79 | -35.20 | 12 |
| Summer | BB | 7.50 | 11.89 | 130.44 | -16.02 | 31.02 | 45 |
| Summer | UK | 0.65 | 12.43 | 152.51 | -23.90 | 25.20 | 345 |
| Summer | MK | -50.88 | 9.71 | 130.44 | -70.08 | -31.67 | 123 |
| Summer | LK | -50.57 | 9.99 | 144.87 | -70.31 | -30.83 | 123 |
| Fall | BB | 21.21 | 11.66 | 120.96 | -1.87 | 44.29 | 45 |
| Fall | UK | 16.04 | 12.14 | 140.95 | -7.97 | 40.05 | 45 |
| Fall | MK | 21.28 | 9.34 | 112.77 | 2.77 | 39.78 | 45 |
| Fall | LK | 4.84 | 9.02 | 98.82 | -13.06 | 22.74 | 45 |
| Inshore | |||||||
| Winter | BB | 46.89 | 12.72 | 167.13 | 21.78 | 72.01 | 3456 |
| Winter | UK | 43.08 | 11.29 | 223.41 | 20.83 | 65.33 | 345 |
| Winter | MK | 55.51 | 11.06 | 209.23 | 33.71 | 77.32 | 56 |
| Winter | LK | 27.89 | 10.00 | 144.14 | 8.13 | 47.65 | 2345 |
| Spring | BB | -90.89 | 12.16 | 139.85 | -114.93 | -66.84 | 1 |
| Spring | UK | -125.12 | 10.66 | 183.59 | -146.14 | -104.10 | 1 |
| Spring | MK | -130.40 | 9.23 | 107.70 | -148.69 | -112.11 | 1 |
| Spring | LK | -131.65 | 9.12 | 103.07 | -149.75 | -113.56 | 1 |
| Summer | BB | -1.40 | 11.89 | 130.44 | -24.92 | 22.12 | 234 |
| Summer | UK | -22.08 | 10.31 | 161.61 | -42.44 | -1.73 | 2 |
| Summer | MK | -98.54 | 9.84 | 137.28 | -118.00 | -79.07 | 1 |
| Summer | LK | -95.74 | 10.30 | 162.27 | -116.08 | -75.39 | 1 |
| Fall | BB | 69.20 | 11.24 | 105.33 | 46.92 | 91.48 | 56 |
| Fall | UK | 93.76 | 10.14 | 153.31 | 73.73 | 113.79 | 6 |
| Fall | MK | 47.65 | 9.34 | 112.55 | 29.14 | 66.16 | 456 |
| Fall | LK | -6.04 | 9.02 | 98.82 | -23.94 | 11.86 | 23 |
dDIC_year.emmc<-emmeans(dDIC_seson, ~Sub_region*Zone)
dDIC_year.emmc<-multcomp::cld(dDIC_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dDIC_year.emmc<-dDIC_year.emmc[order(dDIC_year.emmc$Zone,
dDIC_year.emmc$Sub_region), ]
#dDIC_year.emmc
dDIC_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year dDIC") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | 1.63 | 7.45 | 20.91 | -13.88 | 17.13 | 23 |
| UK | -0.60 | 6.25 | 23.31 | -13.52 | 12.32 | 23 |
| MK | -5.68 | 6.05 | 20.45 | -18.27 | 6.92 | 23 |
| LK | 0.78 | 5.23 | 20.35 | -10.13 | 11.69 | 3 |
| Mid-Channel | ||||||
| BB | 4.37 | 7.60 | 22.67 | -11.36 | 20.11 | 3 |
| UK | -2.66 | 7.71 | 23.94 | -18.57 | 13.25 | 23 |
| MK | -17.71 | 6.05 | 20.43 | -30.30 | -5.12 | 23 |
| LK | -16.86 | 5.94 | 19.05 | -29.29 | -4.42 | 23 |
| Inshore | ||||||
| BB | 5.95 | 7.43 | 20.70 | -9.52 | 21.43 | 3 |
| UK | -2.59 | 6.40 | 25.54 | -15.75 | 10.57 | 23 |
| MK | -31.44 | 6.10 | 21.21 | -44.13 | -18.76 | 12 |
| LK | -51.39 | 5.99 | 19.73 | -63.90 | -38.87 | 1 |
Figure 4B: Model
dDIC_ses_2.emmc<-as.data.frame(dDIC_ses.emmc)
dDIC_ye_3.emmc<-as.data.frame(dDIC_year.emmc)
dDIC_ye_3.emmc$Season<-"Mean"
dDIC_all_4<-rbind(dDIC_ses_2.emmc, dDIC_ye_3.emmc)
dDIC_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearDIC<- ggplot(dDIC_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearDIC
LMER - dDIC Inshore
# Inshore dDIC
hist(inshore.season.data$dDIC_MY)
shapiro.test(inshore.season.data$dDIC_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dDIC_MY
## W = 0.99236, p-value = 0.03032 dDIC_seson_in<-lmer(dDIC_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dDIC_seson_in)
av.dDIC_seson_in<-anova(dDIC_seson_in)
av.dDIC_seson_in ranova(dDIC_seson_in) #step(dDIC_seson_in)
plot(dDIC_seson_in)
par(mfrow=c(1,1))
qqnorm(resid(dDIC_seson_in))
acf(resid(dDIC_seson_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_seson_in, scale = TRUE)
#plot_summs(dDIC_seson_in)
dDIC_ses_in.emmc<-emmeans(dDIC_seson_in, ~ Sub_region*Season)
dDIC_ses_in.emmc<-multcomp::cld(dDIC_ses_in.emmc)
#dDIC_ses_in.emmc<-as.data.frame(dDIC_ses_in.emmc[complete.cases(dDIC_ses_in.emmc),])
dDIC_ses_in.emmc<-dDIC_ses_in.emmc[order(dDIC_ses_in.emmc$Season,
dDIC_ses_in.emmc$Sub_region), ]
dDIC_ses_in.emmcLMER - all dpCO2
# Inshore Omega
hist(season.data$dpCO2_MY)
shapiro.test(season.data$dpCO2_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dpCO2_MY
## W = 0.91083, p-value < 2.2e-16 # dpCO2_seson<-lmer(dpCO2_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dpCO2_seson<-lmer(dpCO2_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dpCO2_seson)
av.dPCO2_seson<-anova(dpCO2_seson)
av.dPCO2_seson ranova(dpCO2_seson) step(dpCO2_seson)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 50 -6588.2 13276
## (1 | SiteID) 0 49 -6591.3 13281 6.2747 1 0.01225 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Zone:Sub_region:Season 1 59111 3284 18 1204.5 1.3615 0.1417094
## Zone:Sub_region 2 13233 2206 6 21.3 0.9096 0.5067938
## Zone:Season 0 599447 99908 6 1224.1 41.2105 < 2.2e-16
## Sub_region:Season 0 68374 7597 9 1223.3 3.1337 0.0009677
##
## Zone:Sub_region:Season
## Zone:Sub_region
## Zone:Season ***
## Sub_region:Season ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dpCO2_MY ~ Zone + Sub_region + Season + (1 | SiteID) + Zone:Season + Sub_region:Season plot(dpCO2_seson)
par(mfrow=c(1,2))
qqnorm(resid(dpCO2_seson))
acf(resid(dpCO2_seson), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dpCO2_seson)
dpCO2_ses.emmc<-emmeans(dpCO2_seson, ~Zone*Sub_region*Season)
dpCO2_ses.emmc<-multcomp::cld(dpCO2_ses.emmc)
#dpCO2_ses.emmc<-as.data.frame(dpCO2_ses.emmc[complete.cases(dpCO2_ses.emmc),])
dpCO2_ses.emmc<-dpCO2_ses.emmc[order(dpCO2_ses.emmc$Zone, dpCO2_ses.emmc$Season), ]
dpCO2_ses.emmc #summ(dpCO2_seson, scale = TRUE)LMER - dPCO2 Inshore
# Inshore dDIC
hist(inshore.season.data$dpCO2_MY)
shapiro.test(inshore.season.data$dpCO2_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dpCO2_MY
## W = 0.98434, p-value = 0.0001655 dpCO2_seson_in<-lmer(dpCO2_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dpCO2_seson_in)
av.dPCO2_seson_in<-anova(dpCO2_seson_in)
av.dPCO2_seson_in ranova(dpCO2_seson_in) #step(dpCO2_seson_in)
plot(dpCO2_seson_in)
par(mfrow=c(1,2))
qqnorm(resid(dpCO2_seson_in))
acf(resid(dpCO2_seson_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dpCO2_seson_in, scale = TRUE)
#plot_summs(dpCO2_seson_in)
dpCO2_ses_in.emmc<-emmeans(dpCO2_seson_in, ~ Sub_region*Season)
dpCO2_ses_in.emmc<-multcomp::cld(dpCO2_ses_in.emmc)
#dpCO2_ses_in.emmc<-as.data.frame(dpCO2_ses_in.emmc[complete.cases(dpCO2_ses_in.emmc),])
dpCO2_ses_in.emmc<-dpCO2_ses_in.emmc[order(dpCO2_ses_in.emmc$Season,
dpCO2_ses_in.emmc$Sub_region), ]
dpCO2_ses_in.emmcLMER - all Omega
# Inshore Omega
hist(season.data$Aragonite_Sat_W)
shapiro.test(season.data$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$Aragonite_Sat_W
## W = 0.98017, p-value = 2.733e-12 # Omega_seson<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
Omega_seson<-lmer(Aragonite_Sat_W~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(Omega_seson)
av.Ome_seson<-anova(Omega_seson)
av.Ome_seson ranova(Omega_seson) #step(Omega_seson)
plot(Omega_seson)
par(mfrow=c(1,2))
qqnorm(resid(Omega_seson))
acf(resid(Omega_seson), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(Omega_seson)
# Ara_ses.emmc<-emmeans(Omega_seson, ~Zone*Sub_region*Season)
# Ara_ses.emmc<-multcomp::cld(Ara_ses.emmc)
# Ara_ses.emmc<-as.data.frame(Ara_ses.emmc[complete.cases(Ara_ses.emmc),])
# Ara_ses.emmc<-Ara_ses.emmc[order(Ara_ses.emmc$Zone, Ara_ses.emmc$Season), ]
# Ara_ses.emmc
Ara_ses.emmc<-emmeans(Omega_seson, ~Zone*Season, by="Sub_region")
Ara_ses.emmc<-multcomp::cld(Ara_ses.emmc)
Ara_ses.emmc Ara_ses.emmc2<-emmeans(Omega_seson, ~Sub_region*Season, by="Zone")
Ara_ses.emmc2<-multcomp::cld(Ara_ses.emmc2)
Ara_ses.emmc2LMER - all dOmega - MODEL 16
# Inshore Omega
hist(season.data$dOme_MY)
shapiro.test(season.data$dOme_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dOme_MY
## W = 0.94515, p-value < 2.2e-16 # Omega_seson<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dOmega_seson<-lmer(dOme_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dOmega_seson)
av.dOme_seson<-anova(dOmega_seson)
av.dOme_seson ranova(dOmega_seson) #step(dOmega_seson)
plot(dOmega_seson)
par(mfrow=c(1,2))
qqnorm(resid(dOmega_seson))
acf(resid(dOmega_seson), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dOmega_seson)
dAra_ses.emmc<-emmeans(dOmega_seson, ~Zone*Season, by="Sub_region")
dAra_ses.emmc<-multcomp::cld(dAra_ses.emmc)
dAra_ses.emmcdAra_ses.emmc1<-emmeans(dOmega_seson, ~Sub_region*Season, by="Zone")
dAra_ses.emmc1<-multcomp::cld(dAra_ses.emmc1)
dAra_ses.emmc1dAra_ses.emmc2<-emmeans(dOmega_seson, ~Season*Sub_region, by="Zone")
dAra_ses.emmc2<-multcomp::cld(dAra_ses.emmc2)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dAra_ses.emmc2<-dAra_ses.emmc2[order(dAra_ses.emmc2$Zone,
dAra_ses.emmc2$Season,
dAra_ses.emmc2$Sub_region), ]
dAra_ses.emmc2[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal d Omega") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 0.02 | 0.07 | 224.53 | -0.12 | 0.16 | 1 |
| Winter | UK | 0.04 | 0.06 | 259.15 | -0.08 | 0.16 | 1 |
| Winter | MK | -0.04 | 0.06 | 231.70 | -0.16 | 0.07 | 1 |
| Winter | LK | -0.07 | 0.05 | 222.35 | -0.17 | 0.03 | 1 |
| Spring | BB | -0.04 | 0.07 | 185.12 | -0.17 | 0.10 | 1 |
| Spring | UK | -0.01 | 0.06 | 216.27 | -0.12 | 0.11 | 1 |
| Spring | MK | 0.06 | 0.05 | 164.28 | -0.04 | 0.17 | 1 |
| Spring | LK | -0.06 | 0.05 | 158.98 | -0.15 | 0.03 | 1 |
| Summer | BB | -0.10 | 0.07 | 173.51 | -0.23 | 0.03 | 1 |
| Summer | UK | -0.03 | 0.06 | 192.38 | -0.14 | 0.08 | 1 |
| Summer | MK | -0.06 | 0.05 | 173.51 | -0.17 | 0.04 | 1 |
| Summer | LK | -0.14 | 0.05 | 196.45 | -0.24 | -0.05 | 1 |
| Fall | BB | -0.04 | 0.06 | 147.68 | -0.17 | 0.08 | 1 |
| Fall | UK | -0.05 | 0.05 | 182.94 | -0.15 | 0.06 | 1 |
| Fall | MK | -0.02 | 0.05 | 141.32 | -0.12 | 0.08 | 1 |
| Fall | LK | -0.06 | 0.04 | 142.64 | -0.15 | 0.03 | 1 |
| Mid-Channel | |||||||
| Winter | BB | -0.04 | 0.07 | 247.53 | -0.19 | 0.10 | 1234 |
| Winter | UK | 0.08 | 0.08 | 272.55 | -0.07 | 0.23 | 2345 |
| Winter | MK | 0.00 | 0.06 | 231.70 | -0.12 | 0.11 | 1234 |
| Winter | LK | -0.19 | 0.05 | 172.72 | -0.29 | -0.08 | 12 |
| Spring | BB | 0.13 | 0.07 | 244.70 | -0.01 | 0.28 | 345 |
| Spring | UK | 0.27 | 0.07 | 224.49 | 0.13 | 0.41 | 45 |
| Spring | MK | 0.27 | 0.05 | 156.01 | 0.17 | 0.37 | 5 |
| Spring | LK | 0.02 | 0.05 | 141.32 | -0.08 | 0.12 | 2345 |
| Summer | BB | -0.22 | 0.07 | 173.51 | -0.35 | -0.09 | 12 |
| Summer | UK | -0.09 | 0.07 | 203.59 | -0.22 | 0.05 | 123 |
| Summer | MK | -0.16 | 0.05 | 173.51 | -0.27 | -0.05 | 123 |
| Summer | LK | -0.27 | 0.06 | 193.60 | -0.38 | -0.16 | 1 |
| Fall | BB | -0.19 | 0.06 | 160.01 | -0.32 | -0.06 | 12 |
| Fall | UK | -0.17 | 0.07 | 188.09 | -0.30 | -0.04 | 123 |
| Fall | MK | -0.17 | 0.05 | 148.58 | -0.27 | -0.07 | 123 |
| Fall | LK | -0.18 | 0.05 | 128.83 | -0.28 | -0.09 | 12 |
| Inshore | |||||||
| Winter | BB | -0.06 | 0.07 | 224.53 | -0.20 | 0.08 | 456 |
| Winter | UK | 0.05 | 0.06 | 300.45 | -0.08 | 0.17 | 56 |
| Winter | MK | 0.03 | 0.06 | 282.20 | -0.09 | 0.15 | 56 |
| Winter | LK | -0.24 | 0.06 | 191.59 | -0.35 | -0.13 | 2345 |
| Spring | BB | 0.24 | 0.07 | 185.16 | 0.11 | 0.37 | 67 |
| Spring | UK | 0.65 | 0.06 | 247.53 | 0.53 | 0.77 | 8 |
| Spring | MK | 0.44 | 0.05 | 141.32 | 0.34 | 0.55 | 78 |
| Spring | LK | -0.01 | 0.05 | 134.77 | -0.11 | 0.09 | 56 |
| Summer | BB | -0.41 | 0.07 | 173.51 | -0.54 | -0.28 | 123 |
| Summer | UK | -0.13 | 0.06 | 216.26 | -0.25 | -0.02 | 345 |
| Summer | MK | -0.36 | 0.05 | 182.97 | -0.47 | -0.25 | 234 |
| Summer | LK | -0.67 | 0.06 | 217.83 | -0.78 | -0.55 | 1 |
| Fall | BB | -0.48 | 0.06 | 137.96 | -0.60 | -0.35 | 12 |
| Fall | UK | -0.51 | 0.06 | 205.56 | -0.62 | -0.40 | 12 |
| Fall | MK | -0.35 | 0.05 | 147.98 | -0.45 | -0.25 | 234 |
| Fall | LK | -0.63 | 0.05 | 128.83 | -0.73 | -0.53 | 1 |
dOm_year.emmc<-emmeans(dOmega_seson, ~Sub_region*Zone)
dOm_year.emmc<-multcomp::cld(dOm_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dOm_year.emmc<-dOm_year.emmc[order(dOm_year.emmc$Zone,
dOm_year.emmc$Sub_region), ]
#dDIC_year.emmc
dOm_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year d Omega") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | -0.04 | 0.04 | 20.83 | -0.12 | 0.04 | 23 |
| UK | -0.01 | 0.03 | 23.93 | -0.08 | 0.06 | 23 |
| MK | -0.02 | 0.03 | 20.28 | -0.08 | 0.05 | 23 |
| LK | -0.08 | 0.03 | 20.57 | -0.14 | -0.03 | 23 |
| Mid-Channel | ||||||
| BB | -0.08 | 0.04 | 23.12 | -0.16 | 0.00 | 23 |
| UK | 0.02 | 0.04 | 24.76 | -0.06 | 0.10 | 23 |
| MK | -0.02 | 0.03 | 20.26 | -0.08 | 0.05 | 23 |
| LK | -0.16 | 0.03 | 18.52 | -0.22 | -0.09 | 2 |
| Inshore | ||||||
| BB | -0.18 | 0.04 | 20.58 | -0.26 | -0.10 | 2 |
| UK | 0.01 | 0.03 | 26.82 | -0.05 | 0.08 | 3 |
| MK | -0.06 | 0.03 | 21.25 | -0.13 | 0.00 | 23 |
| LK | -0.39 | 0.03 | 19.36 | -0.45 | -0.32 | 1 |
Figure 4D: Model
dAra_ses_2.emmc<-as.data.frame(dAra_ses.emmc2)
dAra_ye_3.emmc<-as.data.frame(dOm_year.emmc)
dAra_ye_3.emmc$Season<-"Mean"
dAra_all_4<-rbind(dAra_ses_2.emmc, dAra_ye_3.emmc)
dAra_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearAra<- ggplot(dAra_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
breaks = seq(-6, 6, 0.3),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearAra
LMER - Omega Inshore
# Inshore dDIC
hist(inshore.season.data$Aragonite_Sat_W)
shapiro.test(inshore.season.data$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$Aragonite_Sat_W
## W = 0.98834, p-value = 0.001761 Omega_seson_in<-lmer(Aragonite_Sat_W~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(Omega_seson_in)
av.Ome_seson_in<-anova(Omega_seson_in)
av.Ome_seson_in ranova(Omega_seson_in) #step(Omega_seson_in)
plot(Omega_seson_in)
par(mfrow=c(1,2))
qqnorm(resid(Omega_seson_in))
acf(resid(Omega_seson_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_seson_in, scale = TRUE)
#plot_summs(Omega_seson_in)
omega_ses_in.emmc<-emmeans(Omega_seson_in, ~ Sub_region*Season)
omega_ses_in.emmc<-multcomp::cld(omega_ses_in.emmc)
#omega_ses_in.emmc<-as.data.frame(omega_ses_in.emmc[complete.cases(omega_ses_in.emmc),])
omega_ses_in.emmc<-omega_ses_in.emmc[order(omega_ses_in.emmc$Season,
omega_ses_in.emmc$Sub_region), ]
omega_ses_in.emmcLMER - all pH
# Inshore Omega
hist(season.data$pH_calculated)
shapiro.test(season.data$pH_calculated) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$pH_calculated
## W = 0.98683, p-value = 2.197e-09 # pH_seson<-lmer(pH_calculated~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
pH_seson<-lmer(pH_calculated~ Zone * Sub_region * Season *
(1|SiteID), data=season.data)
#summary(pH_seson)
av.pH_seson<-anova(pH_seson)
av.pH_seson ranova(pH_seson) #step(pH_seson)
plot(pH_seson)
par(mfrow=c(1,2))
qqnorm(resid(pH_seson))
acf(resid(pH_seson), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(pH_seson, scale = TRUE)
#plot_summs(pH_seson)
pH_ses.emmc<-emmeans(pH_seson, ~Zone*Sub_region*Season)
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmc pH_ses.emmc<-emmeans(pH_seson, ~Zone, by = "Sub_region")
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
#pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmc pH_ses.emmc<-emmeans(pH_seson, ~Zone)
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
#pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmcLMER - all dpH - MODEL 15
# Inshore Omega
hist(season.data$dpH_MY)
shapiro.test(season.data$dpH_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dpH_MY
## W = 0.93245, p-value < 2.2e-16 # Omega_seson<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dpH_seson<-lmer(dpH_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dpH_seson)
av.dpH_seson<-anova(dpH_seson)
av.dpH_seson ranova(dpH_seson) #step(dpH_seson)
plot(dpH_seson)
par(mfrow=c(1,2))
qqnorm(resid(dpH_seson))
acf(resid(dpH_seson), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dpH_seson)
dpH_ses.emmc<-emmeans(dpH_seson, ~Zone*Season, by="Sub_region")
dpH_ses.emmc<-multcomp::cld(dpH_ses.emmc)
dpH_ses.emmcdpH_ses.emmc<-emmeans(dpH_seson, ~Sub_region)
dpH_ses.emmc<-multcomp::cld(dpH_ses.emmc)
dpH_ses.emmcdpH_ses.emmc2<-emmeans(dpH_seson, ~Season*Sub_region, by="Zone")
dpH_ses.emmc2<-multcomp::cld(dpH_ses.emmc2)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dpH_ses.emmc2<-dpH_ses.emmc2[order(dpH_ses.emmc2$Zone,
dpH_ses.emmc2$Season,
dpH_ses.emmc2$Sub_region), ]
#dpH_ses.emmc2
dpH_ses.emmc2[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal d pH") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 0.01 | 0.01 | 168.33 | -0.01 | 0.03 | 1 |
| Winter | UK | 0.01 | 0.01 | 195.07 | -0.01 | 0.03 | 1 |
| Winter | MK | 0.00 | 0.01 | 173.54 | -0.01 | 0.02 | 1 |
| Winter | LK | 0.00 | 0.01 | 167.46 | -0.02 | 0.01 | 1 |
| Spring | BB | 0.00 | 0.01 | 140.77 | -0.02 | 0.02 | 1 |
| Spring | UK | 0.00 | 0.01 | 162.76 | -0.02 | 0.02 | 1 |
| Spring | MK | 0.01 | 0.01 | 124.80 | -0.01 | 0.03 | 1 |
| Spring | LK | -0.01 | 0.01 | 121.37 | -0.02 | 0.01 | 1 |
| Summer | BB | -0.02 | 0.01 | 131.33 | -0.04 | 0.00 | 1 |
| Summer | UK | -0.01 | 0.01 | 145.36 | -0.03 | 0.01 | 1 |
| Summer | MK | -0.01 | 0.01 | 131.33 | -0.02 | 0.01 | 1 |
| Summer | LK | -0.02 | 0.01 | 147.92 | -0.03 | 0.00 | 1 |
| Fall | BB | 0.00 | 0.01 | 113.15 | -0.02 | 0.02 | 1 |
| Fall | UK | 0.00 | 0.01 | 138.20 | -0.02 | 0.01 | 1 |
| Fall | MK | 0.00 | 0.01 | 108.38 | -0.01 | 0.02 | 1 |
| Fall | LK | 0.00 | 0.01 | 109.45 | -0.02 | 0.01 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 0.01 | 0.01 | 184.95 | -0.01 | 0.03 | 12345 |
| Winter | UK | 0.03 | 0.01 | 203.65 | 0.00 | 0.05 | 345 |
| Winter | MK | 0.01 | 0.01 | 173.55 | 0.00 | 0.03 | 2345 |
| Winter | LK | -0.01 | 0.01 | 131.04 | -0.03 | 0.00 | 123 |
| Spring | BB | 0.02 | 0.01 | 183.76 | 0.00 | 0.04 | 345 |
| Spring | UK | 0.03 | 0.01 | 168.30 | 0.01 | 0.06 | 45 |
| Spring | MK | 0.04 | 0.01 | 118.88 | 0.02 | 0.05 | 5 |
| Spring | LK | 0.01 | 0.01 | 108.38 | -0.01 | 0.02 | 2345 |
| Summer | BB | -0.03 | 0.01 | 131.33 | -0.05 | -0.01 | 12 |
| Summer | UK | -0.02 | 0.01 | 153.58 | -0.04 | 0.00 | 123 |
| Summer | MK | -0.02 | 0.01 | 131.33 | -0.04 | -0.01 | 123 |
| Summer | LK | -0.03 | 0.01 | 145.88 | -0.05 | -0.02 | 1 |
| Fall | BB | -0.02 | 0.01 | 121.75 | -0.04 | 0.00 | 1234 |
| Fall | UK | -0.01 | 0.01 | 141.92 | -0.03 | 0.01 | 1234 |
| Fall | MK | -0.01 | 0.01 | 113.49 | -0.02 | 0.01 | 1234 |
| Fall | LK | -0.02 | 0.01 | 99.42 | -0.03 | 0.00 | 123 |
| Inshore | |||||||
| Winter | BB | 0.01 | 0.01 | 168.33 | -0.01 | 0.04 | 456 |
| Winter | UK | 0.03 | 0.01 | 225.08 | 0.02 | 0.05 | 67 |
| Winter | MK | 0.02 | 0.01 | 210.79 | 0.00 | 0.04 | 56 |
| Winter | LK | -0.01 | 0.01 | 145.12 | -0.03 | 0.01 | 345 |
| Spring | BB | 0.05 | 0.01 | 140.79 | 0.03 | 0.07 | 67 |
| Spring | UK | 0.10 | 0.01 | 184.95 | 0.08 | 0.11 | 8 |
| Spring | MK | 0.07 | 0.01 | 108.38 | 0.05 | 0.08 | 78 |
| Spring | LK | 0.02 | 0.01 | 103.71 | 0.00 | 0.03 | 56 |
| Summer | BB | -0.06 | 0.01 | 131.33 | -0.08 | -0.04 | 12 |
| Summer | UK | -0.03 | 0.01 | 162.75 | -0.05 | -0.02 | 234 |
| Summer | MK | -0.04 | 0.01 | 138.22 | -0.06 | -0.03 | 123 |
| Summer | LK | -0.08 | 0.01 | 163.43 | -0.10 | -0.06 | 1 |
| Fall | BB | -0.05 | 0.01 | 105.99 | -0.07 | -0.03 | 123 |
| Fall | UK | -0.06 | 0.01 | 154.40 | -0.08 | -0.04 | 12 |
| Fall | MK | -0.03 | 0.01 | 113.27 | -0.04 | -0.01 | 234 |
| Fall | LK | -0.06 | 0.01 | 99.42 | -0.07 | -0.04 | 12 |
dpH_year.emmc<-emmeans(dpH_seson, ~Sub_region*Zone)
dpH_year.emmc<-multcomp::cld(dpH_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dpH_year.emmc<-dpH_year.emmc[order(dpH_year.emmc$Zone,
dpH_year.emmc$Sub_region), ]
#dDIC_year.emmc
dpH_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year d pH") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | 0.00 | 0.01 | 20.87 | -0.02 | 0.01 | 12 |
| UK | 0.00 | 0.01 | 23.28 | -0.01 | 0.01 | 2 |
| MK | 0.00 | 0.01 | 20.40 | -0.01 | 0.01 | 2 |
| LK | -0.01 | 0.00 | 20.66 | -0.02 | 0.00 | 2 |
| Mid-Channel | ||||||
| BB | 0.00 | 0.01 | 22.64 | -0.02 | 0.01 | 12 |
| UK | 0.01 | 0.01 | 23.92 | -0.01 | 0.02 | 2 |
| MK | 0.00 | 0.01 | 20.38 | -0.01 | 0.02 | 2 |
| LK | -0.01 | 0.01 | 19.00 | -0.02 | 0.00 | 12 |
| Inshore | ||||||
| BB | -0.01 | 0.01 | 20.66 | -0.02 | 0.00 | 12 |
| UK | 0.01 | 0.01 | 25.52 | 0.00 | 0.02 | 2 |
| MK | 0.01 | 0.01 | 21.17 | -0.01 | 0.02 | 2 |
| LK | -0.03 | 0.01 | 19.68 | -0.04 | -0.02 | 1 |
Figure 4C: Model
dpH_ses_2.emmc<-as.data.frame(dpH_ses.emmc2)
dpH_ye_3.emmc<-as.data.frame(dpH_year.emmc)
dpH_ye_3.emmc$Season<-"Mean"
dpH_all_4<-rbind(dpH_ses_2.emmc, dpH_ye_3.emmc)
dpH_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearpH<- ggplot(dpH_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.01, 0.01),
breaks = seq(-6, 6, 0.05),
name=(expression (paste(Delta ,
~pH[]))))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearpH
LMER - pH Inshore
# Inshore pH
hist(inshore.season.data$pH_calculated)
shapiro.test(inshore.season.data$pH_calculated) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$pH_calculated
## W = 0.99301, p-value = 0.04501 pH_seson_in<-lmer(pH_calculated~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(pH_seson_in)
av.pH_seson_in<-anova(pH_seson_in)
av.pH_seson_in ranova(pH_seson_in) #step(pH_seson_in)
plot(pH_seson_in)
par(mfrow=c(1,2))
qqnorm(resid(pH_seson_in))
acf(resid(pH_seson_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(pH_seson_in, scale = TRUE)
#plot_summs(pH_seson_in)
pH_ses_in.emmc<-emmeans(pH_seson_in, ~ Sub_region*Season)
pH_ses_in.emmc<-multcomp::cld(pH_ses_in.emmc)
#pH_ses_in.emmc<-as.data.frame(pH_ses_in.emmc[complete.cases(pH_ses_in.emmc),])
pH_ses_in.emmc<-pH_ses_in.emmc[order(pH_ses_in.emmc$Season,
pH_ses_in.emmc$Sub_region), ]
pH_ses_in.emmcSummary all data
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.dTA_seson <- data.frame(av.dTA_seson)
av.dDIC_seson <- data.frame(av.dDIC_seson)
av.dPCO2_seson <- data.frame(av.dPCO2_seson)
av.dOme_seson <- data.frame(av.dOme_seson)
av.dpH_seson <- data.frame(av.dpH_seson)
# Putting all into one dataframe/table
anova_results_all <- data.frame(cbind
(c("Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season"),
rbind(av.dTA_seson, av.dDIC_seson, av.dPCO2_seson,
av.dOme_seson, av.dpH_seson )))
colnames(anova_results_all) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_results_all) <- NULL
# create HTML table using kableExtra
anova_results_all %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "d TA", 1, 7) %>% # groups rows with label
pack_rows(., "d DIC", 8, 14) %>% # groups rows with label
pack_rows(., "d pCO2", 15, 21) %>% # groups rows with label
pack_rows(., "d Omega", 22, 28) %>% # groups rows with label
pack_rows(., "d pH", 29, 35) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| d TA | ||||||
| Zone | 119622.09 | 59811.05 | 2 | 24.12 | 27.05 | 0.00 |
| Sub_region | 225708.04 | 75236.01 | 3 | 23.87 | 34.02 | 0.00 |
| Season | 796380.13 | 265460.04 | 3 | 1213.79 | 120.04 | 0.00 |
| Zone:Sub_region | 172457.19 | 28742.87 | 6 | 23.87 | 13.00 | 0.00 |
| Zone:Season | 519488.58 | 86581.43 | 6 | 1213.80 | 39.15 | 0.00 |
| Sub_region:Season | 125423.53 | 13935.95 | 9 | 1213.74 | 6.30 | 0.00 |
| Zone:Sub_region:Season | 65675.63 | 3648.65 | 18 | 1213.75 | 1.65 | 0.04 |
| d DIC | ||||||
| Zone | 40877.81 | 20438.90 | 2 | 23.32 | 8.82 | 0.00 |
| Sub_region | 77412.09 | 25804.03 | 3 | 23.18 | 11.14 | 0.00 |
| Season | 1334571.06 | 444857.02 | 3 | 1207.98 | 192.02 | 0.00 |
| Zone:Sub_region | 61014.64 | 10169.11 | 6 | 23.18 | 4.39 | 0.00 |
| Zone:Season | 995266.35 | 165877.72 | 6 | 1207.99 | 71.60 | 0.00 |
| Sub_region:Season | 157113.65 | 17457.07 | 9 | 1207.95 | 7.54 | 0.00 |
| Zone:Sub_region:Season | 94802.45 | 5266.80 | 18 | 1207.96 | 2.27 | 0.00 |
| d pCO2 | ||||||
| Zone | 12741.48 | 6370.74 | 2 | 21.66 | 2.64 | 0.09 |
| Sub_region | 58508.24 | 19502.75 | 3 | 21.54 | 8.09 | 0.00 |
| Season | 911341.19 | 303780.40 | 3 | 1204.54 | 125.94 | 0.00 |
| Zone:Sub_region | 13990.74 | 2331.79 | 6 | 21.55 | 0.97 | 0.47 |
| Zone:Season | 607986.17 | 101331.03 | 6 | 1204.55 | 42.01 | 0.00 |
| Sub_region:Season | 68277.65 | 7586.41 | 9 | 1204.50 | 3.15 | 0.00 |
| Zone:Sub_region:Season | 59111.43 | 3283.97 | 18 | 1204.52 | 1.36 | 0.14 |
| d Omega | ||||||
| Zone | 2.11 | 1.05 | 2 | 21.16 | 13.57 | 0.00 |
| Sub_region | 6.49 | 2.16 | 3 | 20.98 | 27.90 | 0.00 |
| Season | 32.54 | 10.85 | 3 | 1204.39 | 139.81 | 0.00 |
| Zone:Sub_region | 2.63 | 0.44 | 6 | 20.99 | 5.64 | 0.00 |
| Zone:Season | 20.31 | 3.39 | 6 | 1204.40 | 43.63 | 0.00 |
| Sub_region:Season | 2.33 | 0.26 | 9 | 1204.34 | 3.33 | 0.00 |
| Zone:Sub_region:Season | 1.73 | 0.10 | 18 | 1204.36 | 1.24 | 0.22 |
| d pH | ||||||
| Zone | 0.00 | 0.00 | 2 | 22.06 | 1.17 | 0.33 |
| Sub_region | 0.07 | 0.02 | 3 | 21.93 | 13.57 | 0.00 |
| Season | 0.66 | 0.22 | 3 | 1204.97 | 133.06 | 0.00 |
| Zone:Sub_region | 0.02 | 0.00 | 6 | 21.93 | 2.45 | 0.06 |
| Zone:Season | 0.39 | 0.06 | 6 | 1204.98 | 38.92 | 0.00 |
| Sub_region:Season | 0.04 | 0.00 | 9 | 1204.93 | 2.62 | 0.01 |
| Zone:Sub_region:Season | 0.03 | 0.00 | 18 | 1204.94 | 1.09 | 0.35 |
#export_summs(dTA_seson, dDIC_seson, dpCO2_seson, scale = TRUE)
Seasonal_models<-list("d TA" = dTA_seson,
"d DIC" = dDIC_seson,
"d pCO2" = dpCO2_seson,
"Omega" = Omega_seson,
"pH" = pH_seson)
modelsummary(Seasonal_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Seasonal and spatial models'#,
#coef_map=factor_order
)| d TA | d DIC | d pCO2 | Omega | pH | |
|---|---|---|---|---|---|
| (Intercept) | 1.046 | −1.155 | −7.598 | 3.699*** | 8.074*** |
| (11.864) | (12.721) | (13.151) | (0.072) | (0.012) | |
| ZoneMid channel | 6.972 | 11.804 | 1.345 | −0.061 | −0.002 |
| (17.018) | (18.225) | (18.834) | (0.103) | (0.017) | |
| ZoneInshore | 39.553* | 48.047** | 4.105 | −0.083 | 0.006 |
| (16.778) | (17.991) | (18.599) | (0.102) | (0.017) | |
| Sub_regionUK | 2.845 | 1.059 | −1.212 | −0.009 | −0.003 |
| (15.625) | (16.728) | (17.286) | (0.095) | (0.015) | |
| Sub_regionMK | 3.551 | 9.089 | 5.808 | −0.057 | −0.004 |
| (15.373) | (16.478) | (17.034) | (0.093) | (0.015) | |
| Sub_regionLK | 0.902 | 8.485 | 9.571 | −0.109 | −0.010 |
| (14.532) | (15.462) | (16.110) | (0.088) | (0.014) | |
| SeasonSpring | −4.947 | 0.623 | 9.930 | 0.056 | −0.036* |
| (15.702) | (16.073) | (16.401) | (0.093) | (0.015) | |
| SeasonSummer | −5.756 | 5.682 | 27.158+ | 0.127 | −0.081*** |
| (15.514) | (15.879) | (16.203) | (0.094) | (0.015) | |
| SeasonFall | −1.087 | 4.823 | 11.334 | 0.023 | −0.042** |
| (15.186) | (15.543) | (15.860) | (0.092) | (0.015) | |
| ZoneMid channelSub_regionUK | −0.207 | −9.979 | −15.897 | 0.121 | 0.020 |
| (23.476) | (25.103) | (25.932) | (0.142) | (0.023) | |
| ZoneInshoreSub_regionUK | 5.292 | −4.871 | −18.249 | 0.106 | 0.020 |
| (22.302) | (23.856) | (24.647) | (0.135) | (0.022) | |
| ZoneMid channelSub_regionMK | 13.666 | 4.728 | −6.319 | 0.118 | 0.014 |
| (21.926) | (23.485) | (24.272) | (0.133) | (0.022) | |
| ZoneInshoreSub_regionMK | 12.846 | −0.469 | −11.129 | 0.157 | 0.014 |
| (22.017) | (23.574) | (24.361) | (0.133) | (0.022) | |
| ZoneMid channelSub_regionLK | 4.618 | 12.666 | 18.821 | −0.037 | −0.007 |
| (20.998) | (22.442) | (23.291) | (0.127) | (0.021) | |
| ZoneInshoreSub_regionLK | −34.186 | −27.490 | 12.977 | −0.069 | −0.011 |
| (20.931) | (22.379) | (23.227) | (0.127) | (0.021) | |
| ZoneMid channelSeasonSpring | −11.689 | −33.140 | −31.214 | 0.215 | 0.031 |
| (22.871) | (23.413) | (23.890) | (0.135) | (0.022) | |
| ZoneInshoreSeasonSpring | −98.226*** | −138.400*** | −67.837** | 0.398** | 0.048* |
| (22.206) | (22.731) | (23.194) | (0.131) | (0.021) | |
| ZoneMid channelSeasonSummer | −13.769 | −8.830 | 16.437 | −0.058 | −0.012 |
| (22.124) | (22.645) | (23.106) | (0.134) | (0.022) | |
| ZoneInshoreSeasonSummer | −72.467*** | −53.973* | 57.295* | −0.223+ | −0.052* |
| (21.940) | (22.457) | (22.915) | (0.133) | (0.022) | |
| ZoneMid channelSeasonFall | −2.906 | 5.739 | 17.134 | −0.089 | −0.013 |
| (21.775) | (22.288) | (22.742) | (0.132) | (0.022) | |
| ZoneInshoreSeasonFall | −16.132 | 17.489 | 64.149** | −0.354** | −0.049* |
| (21.374) | (21.877) | (22.323) | (0.129) | (0.021) | |
| Sub_regionUKSeasonSpring | −0.926 | −2.034 | −1.722 | 0.044 | 0.004 |
| (20.692) | (21.187) | (21.620) | (0.123) | (0.020) | |
| Sub_regionMKSeasonSpring | −7.110 | −22.160 | −21.626 | 0.161 | 0.019 |
| (20.159) | (20.635) | (21.056) | (0.119) | (0.020) | |
| Sub_regionLKSeasonSpring | −8.486 | −14.015 | −4.737 | 0.079 | 0.004 |
| (19.088) | (19.444) | (19.940) | (0.114) | (0.019) | |
| Sub_regionUKSeasonSummer | −3.509 | −8.777 | −7.786 | 0.082 | 0.009 |
| (20.392) | (20.873) | (21.298) | (0.123) | (0.020) | |
| Sub_regionMKSeasonSummer | −20.367 | −31.180 | −21.263 | 0.102 | 0.013 |
| (20.072) | (20.545) | (20.963) | (0.121) | (0.020) | |
| Sub_regionLKSeasonSummer | −6.527 | −12.633 | −7.810 | 0.073 | 0.007 |
| (19.002) | (19.474) | (19.970) | (0.116) | (0.019) | |
| Sub_regionUKSeasonFall | −4.270 | −2.330 | −0.322 | −0.006 | 0.000 |
| (20.077) | (20.551) | (20.970) | (0.121) | (0.020) | |
| Sub_regionMKSeasonFall | −4.739 | −12.228 | −11.203 | 0.078 | 0.011 |
| (19.600) | (20.062) | (20.471) | (0.118) | (0.019) | |
| Sub_regionLKSeasonFall | −4.216 | −10.678 | −9.389 | 0.104 | 0.009 |
| (18.570) | (18.908) | (19.395) | (0.112) | (0.018) | |
| ZoneMid channelSub_regionUKSeasonSpring | 3.883 | 3.766 | 9.527 | 0.014 | −0.017 |
| (31.342) | (32.088) | (32.744) | (0.186) | (0.030) | |
| ZoneInshoreSub_regionUKSeasonSpring | −1.706 | −28.388 | −22.485 | 0.259 | 0.030 |
| (29.533) | (30.236) | (30.853) | (0.175) | (0.029) | |
| ZoneMid channelSub_regionMKSeasonSpring | −38.796 | −35.493 | 1.646 | −0.065 | −0.019 |
| (28.992) | (29.677) | (30.282) | (0.172) | (0.028) | |
| ZoneInshoreSub_regionMKSeasonSpring | −27.635 | −25.974 | 4.715 | −0.060 | −0.008 |
| (28.609) | (29.285) | (29.882) | (0.170) | (0.028) | |
| ZoneMid channelSub_regionLKSeasonSpring | −37.531 | −38.762 | −9.249 | −0.021 | −0.005 |
| (27.924) | (28.519) | (29.169) | (0.166) | (0.027) | |
| ZoneInshoreSub_regionLKSeasonSpring | −20.162 | −7.749 | 6.528 | −0.173 | −0.017 |
| (27.444) | (28.027) | (28.667) | (0.163) | (0.027) | |
| ZoneMid channelSub_regionUKSeasonSummer | 5.033 | 10.845 | 13.219 | −0.059 | −0.015 |
| (30.577) | (31.297) | (31.935) | (0.185) | (0.030) | |
| ZoneInshoreSub_regionUKSeasonSummer | −0.414 | −8.093 | −14.084 | 0.145 | 0.008 |
| (29.102) | (29.787) | (30.395) | (0.176) | (0.029) | |
| ZoneMid channelSub_regionMKSeasonSummer | −46.965+ | −41.015 | −0.563 | −0.097 | −0.016 |
| (28.528) | (29.200) | (29.795) | (0.172) | (0.028) | |
| ZoneInshoreSub_regionMKSeasonSummer | −80.525** | −74.575* | −25.773 | −0.149 | −0.004 |
| (28.642) | (29.316) | (29.914) | (0.173) | (0.028) | |
| ZoneMid channelSub_regionLKSeasonSummer | −54.821* | −66.591* | −30.145 | 0.032 | 0.006 |
| (27.520) | (28.282) | (28.928) | (0.167) | (0.027) | |
| ZoneInshoreSub_regionLKSeasonSummer | −79.637** | −62.700* | −15.649 | −0.147 | −0.006 |
| (27.564) | (28.343) | (28.989) | (0.168) | (0.027) | |
| ZoneMid channelSub_regionUKSeasonFall | −1.908 | 6.079 | 13.033 | −0.107 | −0.015 |
| (30.174) | (30.885) | (31.514) | (0.182) | (0.030) | |
| ZoneInshoreSub_regionUKSeasonFall | 16.349 | 30.699 | 42.284 | −0.133 | −0.028 |
| (28.576) | (29.251) | (29.847) | (0.173) | (0.028) | |
| ZoneMid channelSub_regionMKSeasonFall | −10.458 | −1.525 | 3.017 | −0.118 | −0.011 |
| (27.985) | (28.644) | (29.228) | (0.169) | (0.028) | |
| ZoneInshoreSub_regionMKSeasonFall | −20.494 | −17.950 | −15.134 | −0.051 | 0.002 |
| (27.893) | (28.551) | (29.133) | (0.169) | (0.028) | |
| ZoneMid channelSub_regionLKSeasonFall | −16.475 | −26.842 | −23.332 | 0.062 | 0.012 |
| (26.899) | (27.465) | (28.094) | (0.163) | (0.027) | |
| ZoneInshoreSub_regionLKSeasonFall | −45.957+ | −45.562+ | −21.920 | −0.065 | 0.004 |
| (26.675) | (27.236) | (27.861) | (0.161) | (0.026) | |
| SD (Intercept SiteID) | 4.615 | 7.143 | 7.876 | 0.029 | 0.005 |
| SD (Observations) | 47.026 | 48.132 | 49.113 | 0.284 | 0.046 |
| Num.Obs. | 1279 | 1274 | 1272 | 1286 | 1286 |
| R2 Marg. | 0.460 | 0.512 | 0.363 | 0.428 | 0.477 |
| R2 Cond. | 0.465 | 0.523 | 0.379 | 0.433 | 0.482 |
| AIC | 13236.2 | 13246.6 | 13276.3 | 660.9 | −3824.1 |
| BIC | 13493.9 | 13504.1 | 13533.7 | 918.9 | −3566.1 |
| ICC | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| RMSE | 46.03 | 47.03 | 47.97 | 0.28 | 0.05 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Summary all inshore
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.dTA_seson_in <- data.frame(av.dTA_seson_in)
av.dDIC_seson_in <- data.frame(av.dDIC_seson_in)
av.dPCO2_seson_in <- data.frame(av.dPCO2_seson_in)
av.Ome_seson_in <- data.frame(av.Ome_seson_in)
av.pH_seson_in <- data.frame(av.pH_seson_in)
# Putting all into one dataframe/table
anova_results_In <- data.frame(cbind
(c("Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season"),
rbind(av.dTA_seson_in, av.dDIC_seson_in,
av.dPCO2_seson_in,
av.Ome_seson_in, av.pH_seson_in)))
colnames(anova_results_In) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_results_In) <- NULL
# create HTML table using kableExtra
anova_results_In %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "d TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "d DIC inshore", 4, 6) %>% # groups rows with label
pack_rows(., "d pCO2 inshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega inshore", 10, 12) %>% # groups rows with label
pack_rows(., "pH inshore", 13, 15) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| d TA inshore | ||||||
| Sub_region | 401280.22 | 133760.07 | 3 | 8.33 | 27.61 | 0.00 |
| Season | 1150047.96 | 383349.32 | 3 | 400.68 | 79.12 | 0.00 |
| Sub_region:Season | 120564.92 | 13396.10 | 9 | 400.67 | 2.76 | 0.00 |
| d DIC inshore | ||||||
| Sub_region | 111762.35 | 37254.12 | 3 | 7.77 | 7.26 | 0.01 |
| Season | 2083817.92 | 694605.97 | 3 | 398.06 | 135.42 | 0.00 |
| Sub_region:Season | 156504.72 | 17389.41 | 9 | 398.06 | 3.39 | 0.00 |
| d pCO2 inshore | ||||||
| Sub_region | 42827.75 | 14275.92 | 3 | 7.14 | 2.77 | 0.12 |
| Season | 1317546.80 | 439182.27 | 3 | 397.46 | 85.30 | 0.00 |
| Sub_region:Season | 97882.98 | 10875.89 | 9 | 397.46 | 2.11 | 0.03 |
| Omega inshore | ||||||
| Sub_region | 6.33 | 2.11 | 3 | 6.98 | 15.02 | 0.00 |
| Season | 45.98 | 15.33 | 3 | 403.42 | 109.14 | 0.00 |
| Sub_region:Season | 3.57 | 0.40 | 9 | 403.42 | 2.83 | 0.00 |
| pH inshore | ||||||
| Sub_region | 0.07 | 0.02 | 3 | 7.35 | 6.00 | 0.02 |
| Season | 1.53 | 0.51 | 3 | 403.72 | 130.42 | 0.00 |
| Sub_region:Season | 0.07 | 0.01 | 9 | 403.72 | 1.92 | 0.05 |
#export_summs(dTA_seson, dDIC_seson, dpCO2_seson, scale = TRUE)
Seasonal_models_in<-list("d TA" = dTA_seson_in,
"d DIC" = dDIC_seson_in,
"d pCO2" = dpCO2_seson_in,
"Omega" = Omega_seson_in,
"pH" = pH_seson_in)
modelsummary(Seasonal_models_in, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Seasonal and spatial models for inshore reefs'#,
#coef_map=factor_order
)| d TA | d DIC | d pCO2 | Omega | pH | |
|---|---|---|---|---|---|
| (Intercept) | 40.629* | 46.881* | −3.561 | 3.617*** | 8.080*** |
| (17.194) | (19.028) | (19.810) | (0.098) | (0.016) | |
| Sub_regionUK | 8.138 | −3.796 | −19.308 | 0.096 | 0.017 |
| (23.097) | (25.432) | (26.413) | (0.131) | (0.022) | |
| Sub_regionMK | 16.388 | 8.631 | −5.233 | 0.100 | 0.009 |
| (22.868) | (25.208) | (26.196) | (0.130) | (0.022) | |
| Sub_regionLK | −33.089 | −19.024 | 22.567 | −0.179 | −0.021 |
| (21.813) | (24.202) | (25.227) | (0.124) | (0.021) | |
| SeasonSpring | −103.123*** | −137.797*** | −58.022* | 0.454*** | 0.012 |
| (23.240) | (23.916) | (23.963) | (0.122) | (0.020) | |
| SeasonSummer | −78.253*** | −48.281* | 84.522*** | −0.097 | −0.132*** |
| (22.963) | (23.628) | (23.674) | (0.124) | (0.021) | |
| SeasonFall | −17.227 | 22.315 | 75.501** | −0.331** | −0.091*** |
| (22.263) | (22.907) | (22.951) | (0.120) | (0.020) | |
| Sub_regionUKSeasonSpring | −2.714 | −30.408 | −24.177 | 0.303+ | 0.034 |
| (31.189) | (32.097) | (32.161) | (0.164) | (0.027) | |
| Sub_regionMKSeasonSpring | −34.803 | −48.114 | −16.803 | 0.101 | 0.011 |
| (30.047) | (30.919) | (30.979) | (0.159) | (0.027) | |
| Sub_regionLKSeasonSpring | −28.867 | −21.726 | 1.900 | −0.094 | −0.013 |
| (29.183) | (30.034) | (30.094) | (0.155) | (0.026) | |
| Sub_regionUKSeasonSummer | −3.909 | −16.884 | −21.992 | 0.228 | 0.017 |
| (30.731) | (31.621) | (31.682) | (0.165) | (0.028) | |
| Sub_regionMKSeasonSummer | −100.909*** | −105.767*** | −47.138 | −0.046 | 0.009 |
| (30.242) | (31.118) | (31.179) | (0.163) | (0.027) | |
| Sub_regionLKSeasonSummer | −86.260** | −75.315* | −23.448 | −0.074 | 0.001 |
| (29.553) | (30.642) | (30.702) | (0.160) | (0.027) | |
| Sub_regionUKSeasonFall | 12.055 | 28.360 | 41.860 | −0.138 | −0.029 |
| (30.098) | (30.972) | (31.032) | (0.162) | (0.027) | |
| Sub_regionMKSeasonFall | −25.238 | −30.180 | −26.375 | 0.027 | 0.013 |
| (29.374) | (30.227) | (30.286) | (0.158) | (0.026) | |
| Sub_regionLKSeasonFall | −50.391+ | −56.213+ | −31.278 | 0.039 | 0.013 |
| (28.344) | (29.167) | (29.225) | (0.153) | (0.025) | |
| SD (Intercept SiteID) | 4.602 | 10.976 | 13.375 | 0.051 | 0.009 |
| SD (Observations) | 69.606 | 71.618 | 71.755 | 0.375 | 0.063 |
| Num.Obs. | 422 | 420 | 420 | 426 | 426 |
| R2 Marg. | 0.473 | 0.539 | 0.402 | 0.503 | 0.503 |
| R2 Cond. | 0.476 | 0.550 | 0.422 | 0.511 | 0.514 |
| AIC | 4686.4 | 4690.1 | 4693.1 | 450.5 | −1016.9 |
| BIC | 4759.2 | 4762.8 | 4765.8 | 523.5 | −943.9 |
| ICC | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| RMSE | 68.19 | 69.95 | 70.03 | 0.37 | 0.06 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Figure 4: Models
Figure_4<-ggarrange(DeltaYearTA+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
DeltaYearDIC+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
DeltaYearpH + theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
DeltaYearAra+ theme(legend.position = "bottom",
strip.text.x = element_blank(),
panel.spacing = unit(0.8, "lines")),
labels = c("A", "B", "C", "D"),
ncol = 1, nrow = 4, heights = c(1, 0.9, 0.9, 1.2))
Figure_4
#ggsave(file="FLK_results/Figures/Fig_4/Figure_4.svg", plot=Figure_4, dpi = 300, width=8, height=7)6 LMER long trend MODELS
The F statistic in a regression model is used to test whether there is a linear model of the data - overall , or equivalently that your model is no better than a model with just the intercept.
The t statistic for a coefficient in a linear model tests the H0 that the true coefficient is 0.
*The t-statistic is telling you about each coefficient individually. You can have a model with a low overall linear fit, but one individual coefficient may be relatively contributing a lot to explanation the variation in the DV that is explained on the assumption that there is a linear model.
Data and plots prep
Trends.data<-FLK.data_filtered[FLK.data_filtered$Year>2014, ]
#summary(Trends.data)
Reef.Trends.data<-WS.reef[WS.reef$Year>2014, ]
Reef.Trends.data<-Reef.Trends.data[Reef.Trends.data$Extreme=="Normal", ]
#summary(Reef.Trends.data$Extreme)
Trends.ocean<-Trends.data[Trends.data$Zone=="Oceanic", ]
Trends.offshore<-Reef.Trends.data[Reef.Trends.data$Zone=="Offshore", ]
Trends.midchanel<-Reef.Trends.data[Reef.Trends.data$Zone=="Mid channel", ]
Trends.inshore<-Reef.Trends.data[Reef.Trends.data$Zone=="Inshore", ]
Long_plot<- ggplot(Trends.data) +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank())+
scale_x_date(date_breaks = "2 years",
date_labels = "%Y",
expand = c(0.02, 0.02))
Long_plot2<- ggplot() +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 0, vjust = 0.5))+
scale_x_date(date_breaks = "2 years",
date_labels = "%Y",
expand = c(0.02, 0.02))
xs <- as.Date(as.character("2018-11-20"))
factor_order <- c('Year' = 'Year',
'(Intercept)' = 'Intercept',
'Sub_regionUK' = 'Upper Keys',
'Sub_regionMK' = 'Middle Keys',
'Sub_regionLK' = 'Lower Keys')Omega-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$Aragonite_Sat_W
## W = 0.97568, p-value = 4.918e-14
## Model with all data
Omega_all_o<-lmer(Aragonite_Sat_W~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(Omega_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -319.09 704.18
## (1 | SiteID) 1 32 -319.34 702.67 0.49 1 0.4849
## (1 | Month) 0 31 -506.30 1074.59 373.92 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 0.1753 0.02192 8 1265.1 0.2644 0.97717
## Year:Sub_region 2 0.1009 0.03364 3 1273.3 0.4077 0.74747
## Year:Zone 0 0.6658 0.22195 3 1276.1 2.6936 0.04482
## Zone:Sub_region 0 5.4769 0.68461 8 1276.1 8.3088 4.94e-11
##
## Year:Zone:Sub_region
## Year:Sub_region
## Year:Zone *
## Zone:Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone + Zone:Sub_regionOmega_all_1<-lmer(Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
Omega_all_3<-lmer(Aragonite_Sat_W ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(Omega_all_1, Omega_all_3) summary(Omega_all_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: 575.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6656 -0.5694 0.0463 0.5720 5.0564
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.03497 0.187
## Residual 0.08240 0.287
## Number of obs: 1306, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.490e+01 1.560e+01 1.284e+03 0.955 0.339581
## Year -5.593e-03 7.731e-03 1.284e+03 -0.723 0.469543
## ZoneMid channel 1.716e+01 2.128e+01 1.276e+03 0.806 0.420124
## ZoneOffshore 4.646e+01 2.027e+01 1.276e+03 2.292 0.022080
## ZoneOceanic 6.184e+01 2.675e+01 1.276e+03 2.312 0.020945
## Sub_regionUK 2.151e-01 4.496e-02 1.276e+03 4.784 1.92e-06
## Sub_regionMK 9.895e-02 4.403e-02 1.276e+03 2.247 0.024790
## Sub_regionLK -2.573e-01 4.378e-02 1.276e+03 -5.876 5.36e-09
## Year:ZoneMid channel -8.460e-03 1.055e-02 1.276e+03 -0.802 0.422573
## Year:ZoneOffshore -2.295e-02 1.005e-02 1.276e+03 -2.285 0.022481
## Year:ZoneOceanic -3.044e-02 1.325e-02 1.276e+03 -2.297 0.021795
## ZoneMid channel:Sub_regionUK -1.016e-01 6.705e-02 1.276e+03 -1.515 0.129982
## ZoneOffshore:Sub_regionUK -1.850e-01 6.305e-02 1.276e+03 -2.934 0.003403
## ZoneOceanic:Sub_regionUK -4.041e-01 7.599e-02 1.276e+03 -5.318 1.24e-07
## ZoneMid channel:Sub_regionMK -3.360e-02 6.225e-02 1.276e+03 -0.540 0.589463
## ZoneOffshore:Sub_regionMK -8.265e-02 6.206e-02 1.276e+03 -1.332 0.183203
## ZoneOceanic:Sub_regionMK -3.155e-01 6.396e-02 1.276e+03 -4.933 9.18e-07
## ZoneMid channel:Sub_regionLK 1.714e-01 6.195e-02 1.276e+03 2.767 0.005738
## ZoneOffshore:Sub_regionLK 2.059e-01 6.029e-02 1.276e+03 3.415 0.000658
##
## (Intercept)
## Year
## ZoneMid channel
## ZoneOffshore *
## ZoneOceanic *
## Sub_regionUK ***
## Sub_regionMK *
## Sub_regionLK ***
## Year:ZoneMid channel
## Year:ZoneOffshore *
## Year:ZoneOceanic *
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK **
## ZoneOceanic:Sub_regionUK ***
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK
## ZoneOceanic:Sub_regionMK ***
## ZoneMid channel:Sub_regionLK **
## ZoneOffshore:Sub_regionLK ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(Omega_all_1) ranova(Omega_all_1)# Obtain slopes
ranef(Omega_all_1)## $Month
## (Intercept)
## Apr 0.268648476
## May 0.283884515
## Jun 0.077513882
## Jul 0.008998083
## Aug 0.181905585
## Sep -0.106124564
## Oct -0.016061919
## Nov -0.272080336
## Dec -0.180108577
## Jan -0.231137664
## Feb -0.083923702
## Mar 0.068486220
##
## with conditional variances for "Month" fixef(Omega_all_1)## (Intercept) Year
## 14.904215429 -0.005592579
## ZoneMid channel ZoneOffshore
## 17.161465548 46.457730421
## ZoneOceanic Sub_regionUK
## 61.837031801 0.215079092
## Sub_regionMK Sub_regionLK
## 0.098946926 -0.257272634
## Year:ZoneMid channel Year:ZoneOffshore
## -0.008459574 -0.022952973
## Year:ZoneOceanic ZoneMid channel:Sub_regionUK
## -0.030443872 -0.101596941
## ZoneOffshore:Sub_regionUK ZoneOceanic:Sub_regionUK
## -0.185014457 -0.404066643
## ZoneMid channel:Sub_regionMK ZoneOffshore:Sub_regionMK
## -0.033599868 -0.082648315
## ZoneOceanic:Sub_regionMK ZoneMid channel:Sub_regionLK
## -0.315507684 0.171425236
## ZoneOffshore:Sub_regionLK
## 0.205899946 coef(Omega_all_1)$Month coef(Omega_all_1)$Zone## NULL m.lst <- lstrends(Omega_all_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore -0.00559 0.00774 1284 -0.0208 0.00958
## Mid channel -0.01405 0.00798 1284 -0.0297 0.00160
## Offshore -0.02855 0.00729 1284 -0.0428 -0.01424
## Oceanic -0.03604 0.01129 1280 -0.0582 -0.01388
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel 0.00846 0.0105 1276 0.802 0.8534
## Inshore - Offshore 0.02295 0.0100 1276 2.285 0.1021
## Inshore - Oceanic 0.03044 0.0133 1276 2.297 0.0993
## Mid channel - Offshore 0.01449 0.0102 1276 1.416 0.4895
## Mid channel - Oceanic 0.02198 0.0134 1276 1.641 0.3560
## Offshore - Oceanic 0.00749 0.0130 1276 0.576 0.9393
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesInshore
# Inshore Omega
hist(Trends.inshore$Aragonite_Sat_W)
shapiro.test(Trends.inshore$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$Aragonite_Sat_W
## W = 0.98599, p-value = 0.001134 Omega_in_0<-lmer(Aragonite_Sat_W~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
Omega_in_1<-lmer(Aragonite_Sat_W~Year * Sub_region * Season +
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(Omega_in_0)
#summary(Omega_in_1)
#anova(Omega_in_0, Omega_in_1)
#step(Omega_in_1)
anova(Omega_in_0) ranova(Omega_in_0) step(Omega_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -158.47 338.94
## (1 | SiteID) 1 10 -159.74 339.47 2.53 1 0.1117
## (1 | Month) 0 9 -292.29 602.58 265.11 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.0569 0.0190 3 354.17 0.1691 0.9172
## Year 2 0.1096 0.1096 1 364.53 0.9841 0.3218
## Sub_region 0 10.9897 3.6632 3 358.15 32.8925 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Sub_region + (1 | Month) Omega_in<-lmer(Aragonite_Sat_W ~ Sub_region + (1 | Month),
data=Trends.inshore)
anova(Omega_in) summary(Omega_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 296.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8609 -0.5681 0.0603 0.5554 4.0778
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.1496 0.3868
## Residual 0.1114 0.3337
## Number of obs: 373, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.63208 0.11850 13.30859 30.651 9.81e-14 ***
## Sub_regionUK 0.20027 0.05227 358.12645 3.832 0.00015 ***
## Sub_regionMK 0.09924 0.05124 358.13089 1.937 0.05359 .
## Sub_regionLK -0.24632 0.05098 358.14454 -4.831 2.02e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.247
## Sub_reginMK -0.253 0.571
## Sub_reginLK -0.255 0.572 0.587 plot(Omega_in)
qqnorm(resid(Omega_in))
acf(resid(Omega_in), main="acf(resid(Omega Inshore))")
Arg_in.emmc<-emmeans(Omega_in, ~Sub_region)
Arg_in.emmc<-multcomp::cld(Arg_in.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_in.emmc<-Arg_in.emmc[order(Arg_in.emmc$Sub_region),]
Arg_in.emmc #write.csv(Arg_in.emmc, "FLK_results/Model_9_LTpH_POST.csv")
round(Arg_in.emmc$emmean, digits = 2)## [1] 3.63 3.83 3.73 3.39 #anova(Omega_in)
#ranova(Omega_in)# Create a new data frame for independent variables
In_Newdata<-expand.grid(Zone=unique(Trends.inshore$Zone),
Sub_region=unique(Trends.inshore$Sub_region),
Year=seq((min(Trends.inshore$Year)-0), (max(Trends.inshore$Year)+1)))
In_Omega_Pred <- predict(Omega_in, newdata=In_Newdata, re.form=NA, level=0, asList = FALSE)
In_Omega_Pred <-cbind(In_Newdata, In_Omega_Pred)
In_Omega_Pred$Date<-as.Date(as.character(In_Omega_Pred$Year), format = "%Y")
# Plot raw data point and fitted model
In_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(0,6),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(2.3, 5.2),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=21)+
geom_line(data=In_Omega_Pred, aes(y = In_Omega_Pred,
x=Date, colour = Sub_region), linetype=2, size=1)
In_Omega_Modeled
Mid channel
hist(Trends.midchanel$Aragonite_Sat_W)
shapiro.test(Trends.midchanel$Aragonite_Sat_W)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$Aragonite_Sat_W
## W = 0.98399, p-value = 0.0006581Omega_mc_0<-lmer(Aragonite_Sat_W~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
# Omega_mc_1<-lmer(Aragonite_Sat_W~Year * Sub_region * Season +
# #(1|Zone)+
# (1|SiteID), data=Trends.midchanel)
#summary(Omega_mc_0)
#summary(Omega_mc_1)
anova(Omega_mc_0)#anova(Omega_mc_1)
#anova(Omega_mc_0, Omega_mc_1)
ranova(Omega_mc_0)step(Omega_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -33.105 88.211
## (1 | SiteID) 1 10 -33.637 87.274 1.063 1 0.3025
## (1 | Month) 0 9 -102.380 222.760 137.486 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.12890 0.04297 3 329.13 0.7493 0.52339
## Year 0 0.24196 0.24196 1 342.10 4.2296 0.04048 *
## Sub_region 0 1.99759 0.66586 3 332.22 11.6397 2.849e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + Sub_region + (1 | Month)Omega_mc<-lmer(Aragonite_Sat_W ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(Omega_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 50.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2542 -0.5546 0.0284 0.5818 3.4212
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.04181 0.2045
## Residual 0.05721 0.2392
## Number of obs: 348, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.412104 14.931233 342.106233 2.305 0.02178 *
## Year -0.015216 0.007399 342.099332 -2.057 0.04048 *
## Sub_regionUK 0.115884 0.041502 332.216472 2.792 0.00554 **
## Sub_regionMK 0.065034 0.036727 332.152378 1.771 0.07752 .
## Sub_regionLK -0.085215 0.036655 332.240537 -2.325 0.02069 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.012 0.011
## Sub_reginMK -0.002 0.000 0.530
## Sub_reginLK -0.018 0.016 0.532 0.601 anova(Omega_mc) plot(Omega_mc)
qqnorm(resid(Omega_mc))
acf(resid(Omega_mc), main="acf(resid(Omega mid Channel))") 
Arg_md.emmc<-emmeans(Omega_mc, ~Sub_region+Year)
Arg_md.emmc<-multcomp::cld(Arg_md.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_md.emmc<-Arg_md.emmc[order(Arg_md.emmc$Sub_region),]
Arg_md.emmc #write.csv(Arg_md.emmc, "FLK_results/Model_10_LTpH_POST.csv")# Create a new data frame for independent variables
Mc_Newdata<-expand.grid(Zone=unique(Trends.midchanel$Zone),
Sub_region=unique(Trends.midchanel$Sub_region),
Year=seq((min(Trends.midchanel$Year)-0), (max(Trends.midchanel$Year)+1)))
Mc_Omega_Pred <- predict(Omega_mc, newdata=Mc_Newdata, re.form=NA, level=0, asList = FALSE)
Mc_Omega_Pred <-cbind(Mc_Newdata, Mc_Omega_Pred)
Mc_Omega_Pred$Date<-as.Date(as.character(Mc_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(
summary(Omega_mc)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.midchanel$Aragonite_Sat_W, na.rm =T)+ 0.3
# Plot raw data point and fitted model
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
Mc_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(2.3, 5.2),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_jitter(data=Trends.midchanel,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_Omega_Pred, aes(y = Mc_Omega_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
# geom_text(data = slope, aes(x = xs, y = ys,
# label = (paste("~Omega[Ar]", "=",
# slope, "Year ^-1"))), size=10)
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_Omega_Modeled
Offshore
hist(Trends.offshore$Aragonite_Sat_W)
shapiro.test(Trends.offshore$Aragonite_Sat_W)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$Aragonite_Sat_W
## W = 0.99121, p-value = 0.0128Omega_of_0<-lmer(Aragonite_Sat_W~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(Omega_of_0)
anova(Omega_of_0)ranova(Omega_of_0)step(Omega_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 153.652 -285.30
## (1 | SiteID) 1 10 153.486 -286.97 0.332 1 0.5644
## (1 | Month) 0 9 86.497 -154.99 133.977 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.04838 0.01613 3 404.94 0.6907 0.5581
## Year 0 0.98863 0.98863 1 418.90 42.4403 2.093e-10 ***
## Sub_region 0 0.55609 0.18536 3 408.08 7.9574 3.619e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + Sub_region + (1 | Month)Omega_of<-lmer(Aragonite_Sat_W ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(Omega_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: -327.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8494 -0.6878 0.0746 0.6305 3.5033
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.01341 0.1158
## Residual 0.02329 0.1526
## Number of obs: 424, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.846389 8.611290 418.898403 6.950 1.41e-11 ***
## Year -0.027797 0.004267 418.895440 -6.515 2.09e-10 ***
## Sub_regionUK 0.019509 0.023509 407.966516 0.830 0.40710
## Sub_regionMK 0.017049 0.023277 407.960709 0.732 0.46432
## Sub_regionLK -0.062086 0.022129 408.171391 -2.806 0.00526 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.004 0.002
## Sub_reginMK -0.005 0.003 0.591
## Sub_reginLK -0.013 0.012 0.623 0.630 anova(Omega_of) plot(Omega_of)
qqnorm(resid(Omega_of))
acf(resid(Omega_of), main="acf(resid(Omega offshore))") 
Arg_of.emmc<-emmeans(Omega_of, ~Sub_region+Year)
Arg_of.emmc<-multcomp::cld(Arg_of.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_of.emmc<-Arg_of.emmc[order(Arg_of.emmc$Sub_region),]
Arg_of.emmc#write.csv(Arg_of.emmc, "FLK_results/Model_11_LTpH_POST.csv")# Create a new data frame for independent variables
Of_Newdata<-expand.grid(one=unique(Trends.offshore$Zone),
Sub_region=unique(Trends.offshore$Sub_region),
Year=seq((min(Trends.offshore$Year)-0), (max(Trends.offshore$Year)+1)))
Of_Omega_Pred <- predict(Omega_of, newdata=Of_Newdata, re.form=NA, level=0, asList = FALSE)
Of_Omega_Pred <-cbind(Of_Newdata, Of_Omega_Pred)
Of_Omega_Pred$Date<-as.Date(as.character(Of_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(Omega_of)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.offshore$Aragonite_Sat_W, na.rm =T)
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.offshore,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_Omega_Pred, aes(y = Of_Omega_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_Omega_Modeled
Oceanic
hist(Trends.ocean$Aragonite_Sat_W)
shapiro.test(Trends.ocean$Aragonite_Sat_W)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$Aragonite_Sat_W
## W = 0.9889, p-value = 0.2355Omega_oc_0<-lmer(Aragonite_Sat_W ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
Omega_oc_1<-lmer(Aragonite_Sat_W ~ Year * Sub_region * Season++ (1|SiteID),
data=Trends.ocean)
#summary(Omega_oc_0)
#summary(Omega_oc_1)
#anova(Omega_oc_1)
anova(Omega_oc_0) ranova(Omega_oc_0) step(Omega_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 57.434 -96.867
## (1 | SiteID) 1 8 57.434 -98.867 0.000 1 0.9999
## (1 | Month) 0 7 28.818 -43.635 57.232 1 3.873e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.00575 0.00287 2 144.13 0.1464 0.8640
## Sub_region 2 0.06918 0.03459 2 146.00 1.7828 0.1718
## Year 0 0.54008 0.54008 1 156.35 27.5477 4.925e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + (1 | Month)Omega_oc<-lmer(Aragonite_Sat_W ~ Year + (1 | Month), data = Trends.ocean)
summary(Omega_oc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: -135.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4346 -0.6808 0.0680 0.5175 2.4440
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.01649 0.1284
## Residual 0.01961 0.1400
## Number of obs: 161, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 69.402759 12.504422 156.361546 5.550 1.19e-07 ***
## Year -0.032522 0.006196 156.354492 -5.249 4.92e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(Omega_oc) plot(Omega_oc)
qqnorm(resid(Omega_oc))
acf(resid(Omega_oc), main="acf(resid(Omega open))")
Arg_oc.emmc<-emmeans(Omega_oc, ~Year)
#Arg_oc.emmc<-multcomp::cld(Arg_oc.emmc)
#Arg_oc.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
#Arg_oc.emmc<-Arg_oc.emmc[order(Arg_oc.emmc$Sub_region),]
#Arg_oc.emmc# Create a new data frame for independent variables
Oc_Newdata<-expand.grid(one=unique(Trends.ocean$Zone),
Sub_region=unique(Trends.ocean$Sub_region),
Year=seq((min(Trends.ocean$Year)-0), (max(Trends.ocean$Year)+1)))
Oc_Omega_Pred <- predict(Omega_oc, newdata=Oc_Newdata, re.form=NA, level=0, asList = FALSE)
Oc_Omega_Pred <-cbind(Oc_Newdata, Oc_Omega_Pred)
Oc_Omega_Pred$Date<-as.Date(as.character(Oc_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(Omega_oc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
#ys <- max(Trends.inshore$Aragonite_Sat_W, na.rm =T)
ys <- max(Trends.offshore$Aragonite_Sat_W, na.rm =T)
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.ocean,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_Omega_Pred, aes(y = Oc_Omega_Pred,
x=Date), linetype=1, size=1, colour = "black") +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_Omega_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.Omega_in_0 <- data.frame (anova(Omega_in_0))
av.Omega_mc_0 <- data.frame (anova(Omega_mc_0) )
av.Omega_of_0 <- data.frame (anova(Omega_of_0))
av.Omega_oc_0 <- data.frame (anova(Omega_oc_0))
# Putting all into one dataframe/table
anova_Omega_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.Omega_in_0, av.Omega_mc_0,
av.Omega_of_0, av.Omega_oc_0)))
colnames(anova_Omega_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_Omega_0) <- NULL
# create HTML table using kableExtra
anova_Omega_0 %>% kable("html", digits=2,
caption="Initial omega models fixed factors: Year*Region") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| Year | 0.14 | 0.14 | 1 | 355.21 | 1.24 | 0.27 |
| Sub_region | 0.06 | 0.02 | 3 | 347.58 | 0.19 | 0.90 |
| Year:Sub_region | 0.06 | 0.02 | 3 | 347.57 | 0.19 | 0.90 |
| Omega mid-channel | ||||||
| Year | 0.30 | 0.30 | 1 | 333.24 | 5.30 | 0.02 |
| Sub_region | 0.13 | 0.04 | 3 | 323.51 | 0.77 | 0.51 |
| Year:Sub_region | 0.13 | 0.04 | 3 | 323.51 | 0.77 | 0.51 |
| Omega offshore | ||||||
| Year | 0.99 | 0.99 | 1 | 408.07 | 42.77 | 0.00 |
| Sub_region | 0.05 | 0.02 | 3 | 397.30 | 0.70 | 0.55 |
| Year:Sub_region | 0.05 | 0.02 | 3 | 397.30 | 0.69 | 0.56 |
| Omega ocean | ||||||
| Year | 0.51 | 0.51 | 1 | 151.91 | 26.14 | 0.00 |
| Sub_region | 0.01 | 0.00 | 2 | 144.15 | 0.15 | 0.86 |
| Year:Sub_region | 0.01 | 0.00 | 2 | 144.16 | 0.15 | 0.86 |
# R-ANOVAs
rav.Omega_in_0 <- data.frame (ranova(Omega_in_0))
rav.Omega_mc_0 <- data.frame (ranova(Omega_mc_0) )
rav.Omega_of_0 <- data.frame (ranova(Omega_of_0))
rav.Omega_oc_0 <- data.frame (ranova(Omega_oc_0))
# Putting all into one dataframe/table
ranova_Omega_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.Omega_in_0, rav.Omega_mc_0,
rav.Omega_of_0, rav.Omega_oc_0)))
colnames(ranova_Omega_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_Omega_0) <- NULL
# create HTML table using kableExtra
ranova_Omega_0 %>% kable("html", digits=2,
caption="Initial omega models random factors: Month+Station") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| <none> | 11 | -158.47 | 338.94 | |||
| (1 | Month) | 10 | -292.29 | 604.58 | 267.64 | 1 | 0.00 |
| (1 | SiteID) | 10 | -159.73 | 339.47 | 2.53 | 1 | 0.11 |
| Omega mid-channel | ||||||
| <none> | 11 | -33.11 | 88.21 | |||
| (1 | Month) | 10 | -102.37 | 224.74 | 138.53 | 1 | 0.00 |
| (1 | SiteID) | 10 | -33.64 | 87.27 | 1.06 | 1 | 0.30 |
| Omega offshore | ||||||
| <none> | 11 | 153.65 | -285.30 | |||
| (1 | Month) | 10 | 86.50 | -152.99 | 134.31 | 1 | 0.00 |
| (1 | SiteID) | 10 | 153.49 | -286.97 | 0.33 | 1 | 0.56 |
| Omega ocean | ||||||
| <none> | 9 | 57.43 | -96.87 | |||
| (1 | Month) | 8 | 28.82 | -41.64 | 57.23 | 1 | 0.00 |
| (1 | SiteID) | 8 | 57.43 | -98.87 | 0.00 | 1 | 1.00 |
Omega_models_0<-list("Inshore" = Omega_in_0,
"Mid channel" = Omega_mc_0,
"Offshore" = Omega_of_0,
"Ocean" = Omega_oc_0)
modelsummary(Omega_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Initial Omega model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.021 | −0.022 | −0.030** | −0.038** |
| (0.020) | (0.014) | (0.009) | (0.013) | |
| Intercept | 45.736 | 48.779+ | 64.605*** | 80.284** |
| (39.883) | (28.977) | (18.437) | (25.914) | |
| Upper Keys | −13.532 | 16.828 | −7.316 | |
| (51.587) | (40.622) | (23.282) | ||
| Middle Keys | −31.215 | −22.537 | 10.487 | −10.925 |
| (50.015) | (36.040) | (23.075) | (30.349) | |
| Lower Keys | −32.891 | −35.223 | −16.918 | −16.583 |
| (50.313) | (36.356) | (21.984) | (30.572) | |
| Num.Obs. | 373 | 348 | 424 | 161 |
| R2 Marg. | 0.102 | 0.066 | 0.111 | 0.202 |
| R2 Cond. | 0.628 | 0.469 | 0.441 | |
| AIC | 338.9 | 88.2 | −285.3 | −96.9 |
| BIC | 382.1 | 130.6 | −240.8 | −69.1 |
| ICC | 0.6 | 0.4 | 0.4 | |
| RMSE | 0.32 | 0.23 | 0.15 | 0.13 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Models with non-significant factors removed
Omega_models<-list("Inshore" = Omega_in,
"Mid channel" = Omega_mc,
"Offshore" = Omega_of,
"Ocean" = Omega_oc)
modelsummary(Omega_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Final Omega model outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.015* | −0.028*** | −0.033*** | |
| (0.007) | (0.004) | (0.006) | ||
| Intercept | 3.632*** | 34.412* | 59.846*** | 69.403*** |
| (0.118) | (14.931) | (8.611) | (12.504) | |
| Upper Keys | 0.200*** | 0.116** | 0.020 | |
| (0.052) | (0.042) | (0.024) | ||
| Middle Keys | 0.099+ | 0.065+ | 0.017 | |
| (0.051) | (0.037) | (0.023) | ||
| Lower Keys | −0.246*** | −0.085* | −0.062** | |
| (0.051) | (0.037) | (0.022) | ||
| Num.Obs. | 373 | 348 | 424 | 161 |
| R2 Marg. | 0.102 | 0.064 | 0.109 | 0.110 |
| R2 Cond. | 0.617 | 0.459 | 0.435 | 0.517 |
| AIC | 308.1 | 64.3 | −313.4 | −127.6 |
| BIC | 331.7 | 91.3 | −285.0 | −115.3 |
| ICC | 0.6 | 0.4 | 0.4 | 0.5 |
| RMSE | 0.33 | 0.23 | 0.15 | 0.13 |
| + p |
modelsummary(Omega_models, output = "FLK_results/Figures/Fig_2/Trend_Omega.docx")
#modelsummary(Omega_models, estimate = "p.value")Trends_Omega<-ggarrange(In_Omega_Modeled+ theme(
#axis.text.x=element_blank(),
legend.position = "none"),
Mc_Omega_Modeled+ theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Of_Omega_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Oc_Omega_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
labels = c("C"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_Omega
pCO2-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$pCO2_uatm
## W = 0.96584, p-value < 2.2e-16
## Model with all data
pCO2_all_o<-lmer(pCO2_uatm~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(Omega_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -319.09 704.18
## (1 | SiteID) 1 32 -319.34 702.67 0.49 1 0.4849
## (1 | Month) 0 31 -506.30 1074.59 373.92 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 0.1753 0.02192 8 1265.1 0.2644 0.97717
## Year:Sub_region 2 0.1009 0.03364 3 1273.3 0.4077 0.74747
## Year:Zone 0 0.6658 0.22195 3 1276.1 2.6936 0.04482
## Zone:Sub_region 0 5.4769 0.68461 8 1276.1 8.3088 4.94e-11
##
## Year:Zone:Sub_region
## Year:Sub_region
## Year:Zone *
## Zone:Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone + Zone:Sub_regionpCO2_all_o_1<-lmer(pCO2_uatm ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
pCO2_all_o_3<-lmer(pCO2_uatm ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(pCO2_all_o, pCO2_all_o_1, pCO2_all_o_3) summary(pCO2_all_o_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: 13976.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3622 -0.5534 -0.0761 0.5213 5.6841
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 2727 52.22
## Residual 2721 52.17
## Number of obs: 1306, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2370.784 2838.709 1280.456 -0.835 0.403782
## Year 1.394 1.407 1280.396 0.991 0.321990
## ZoneMid channel -5726.375 3867.277 1276.066 -1.481 0.138927
## ZoneOffshore -10230.979 3684.011 1276.079 -2.777 0.005564
## ZoneOceanic -13407.538 4861.031 1275.987 -2.758 0.005896
## Sub_regionUK -26.986 8.171 1275.986 -3.303 0.000984
## Sub_regionMK -29.743 8.002 1275.980 -3.717 0.000210
## Sub_regionLK 8.370 7.957 1275.988 1.052 0.293031
## Year:ZoneMid channel 2.828 1.916 1276.066 1.476 0.140219
## Year:ZoneOffshore 5.059 1.826 1276.079 2.771 0.005664
## Year:ZoneOceanic 6.629 2.409 1275.987 2.752 0.006008
## ZoneMid channel:Sub_regionUK 15.346 12.186 1275.996 1.259 0.208144
## ZoneOffshore:Sub_regionUK 22.302 11.459 1275.981 1.946 0.051846
## ZoneOceanic:Sub_regionUK 26.092 13.810 1275.998 1.889 0.059066
## ZoneMid channel:Sub_regionMK 17.202 11.313 1275.959 1.521 0.128629
## ZoneOffshore:Sub_regionMK 22.904 11.279 1275.960 2.031 0.042497
## ZoneOceanic:Sub_regionMK 32.224 11.624 1275.968 2.772 0.005651
## ZoneMid channel:Sub_regionLK -3.244 11.259 1275.957 -0.288 0.773308
## ZoneOffshore:Sub_regionLK -5.120 10.958 1275.960 -0.467 0.640369
##
## (Intercept)
## Year
## ZoneMid channel
## ZoneOffshore **
## ZoneOceanic **
## Sub_regionUK ***
## Sub_regionMK ***
## Sub_regionLK
## Year:ZoneMid channel
## Year:ZoneOffshore **
## Year:ZoneOceanic **
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK .
## ZoneOceanic:Sub_regionUK .
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK *
## ZoneOceanic:Sub_regionMK **
## ZoneMid channel:Sub_regionLK
## ZoneOffshore:Sub_regionLK
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(pCO2_all_o_1) ranova(pCO2_all_o_1)# Obtain slopes
ranef(pCO2_all_o_1)## $Month
## (Intercept)
## Apr -63.065458
## May -60.058921
## Jun 4.230287
## Jul 56.360019
## Aug 26.407851
## Sep 78.531848
## Oct 41.719135
## Nov 54.666768
## Dec -8.519955
## Jan -13.722468
## Feb -69.453622
## Mar -47.095484
##
## with conditional variances for "Month" fixef(pCO2_all_o_1)## (Intercept) Year
## -2370.784285 1.393637
## ZoneMid channel ZoneOffshore
## -5726.374554 -10230.978644
## ZoneOceanic Sub_regionUK
## -13407.538357 -26.986185
## Sub_regionMK Sub_regionLK
## -29.742977 8.370348
## Year:ZoneMid channel Year:ZoneOffshore
## 2.828433 5.059307
## Year:ZoneOceanic ZoneMid channel:Sub_regionUK
## 6.629285 15.346269
## ZoneOffshore:Sub_regionUK ZoneOceanic:Sub_regionUK
## 22.301737 26.091559
## ZoneMid channel:Sub_regionMK ZoneOffshore:Sub_regionMK
## 17.201669 22.903774
## ZoneOceanic:Sub_regionMK ZoneMid channel:Sub_regionLK
## 32.223667 -3.243754
## ZoneOffshore:Sub_regionLK
## -5.120408 coef(pCO2_all_o_1)$Month coef(pCO2_all_o_1)$Zone## NULL m.lst <- lstrends(pCO2_all_o_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore 1.39 1.41 1280 -1.37 4.15
## Mid channel 4.22 1.45 1280 1.38 7.07
## Offshore 6.45 1.33 1280 3.85 9.05
## Oceanic 8.02 2.05 1278 4.00 12.05
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel -2.83 1.92 1276 -1.476 0.4524
## Inshore - Offshore -5.06 1.83 1276 -2.771 0.0289
## Inshore - Oceanic -6.63 2.41 1276 -2.752 0.0306
## Mid channel - Offshore -2.23 1.86 1276 -1.199 0.6274
## Mid channel - Oceanic -3.80 2.43 1276 -1.561 0.4014
## Offshore - Oceanic -1.57 2.36 1276 -0.664 0.9106
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesInshore
# Inshore pCO2
hist(Trends.inshore$pCO2_uatm)
shapiro.test(Trends.inshore$pCO2_uatm)# Normality NOT? rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$pCO2_uatm
## W = 0.99227, p-value = 0.0502 pCO2_in_0<-lmer(pCO2_uatm~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(pCO2_in_0)
anova(pCO2_in_0) ranova(pCO2_in_0) step(pCO2_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -2081.9 4185.7
## (1 | Month) 0 10 -2239.1 4498.2 314.514 1 < 2e-16 ***
## (1 | SiteID) 0 10 -2084.8 4189.7 5.948 1 0.01473 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 11398.4 3799.5 3 347.42 0.9312 0.4257
## Year 2 3465.1 3465.1 1 356.71 0.8494 0.3573
## Sub_region 3 30987.8 10329.3 3 6.99 2.5318 0.1406
##
## Model found:
## pCO2_uatm ~ (1 | Month) + (1 | SiteID)pCO2_in<-lmer(pCO2_uatm ~ (1|Month)+(1|SiteID), data=Trends.inshore)
summary(pCO2_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 4212.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6141 -0.6364 -0.0455 0.5782 3.8147
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 7423 86.16
## SiteID (Intercept) 395 19.88
## Residual 4079 63.87
## Number of obs: 373, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 427.28 25.82 12.22 16.55 9.77e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 anova(pCO2_in) plot(pCO2_in)
qqnorm(resid(pCO2_in))
acf(resid(pCO2_in), main="acf(resid(pCO2 Inshore))") 
#pCO2_in.emmc<-emmeans(pCO2_in, ~Month)
#pCO2_in.emmc<-multcomp::cld(pCO2_in.emmc)
#pCO2_in.emmc<-pCO2_in.emmc[order(pCO2_in.emmc$Sub_region),]
#pCO2_in.emmc# Predict values
In_pCO2_Pred <- predict(pCO2_in, newdata=In_Newdata, re.form=NA, level=0, asList = FALSE)
In_pCO2_Pred <-cbind(In_Newdata, In_pCO2_Pred)
In_pCO2_Pred$Date<-as.Date(as.character(In_pCO2_Pred$Year), format = "%Y")
# # Model summary
# slope<-round(
# summary(pCO2_in)$coefficients["Year", "Estimate"], digits = 3)
# xs <- as.Date(as.character("2018-11-20"))
# ys <- max(Trends.midchanel$pCO2_uatm, na.rm =T)
#
# # Plot raw data point and fitted model
# plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
# plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
In_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(200,800),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=pCO2_uatm, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=21)+
geom_line(data=In_pCO2_Pred, aes(y = In_pCO2_Pred,
x=Date), linetype=2, size=1, colour = "black")
In_pCO2_Modeled
Mid channel
hist(Trends.midchanel$pCO2_uatm)
shapiro.test(Trends.midchanel$pCO2_uatm)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$pCO2_uatm
## W = 0.99235, p-value = 0.07115pCO2_mc_0<-lmer(pCO2_uatm~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(pCO2_mc_0)
anova(pCO2_mc_0) ranova(pCO2_mc_0) step(pCO2_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1786.3 3594.5
## (1 | SiteID) 1 10 -1786.5 3592.9 0.361 1 0.5477
## (1 | Month) 0 9 -1908.4 3834.8 243.885 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 6492.3 2164.1 3 328.96 1.2852 0.279385
## Year 0 16354.6 16354.6 1 338.48 9.6873 0.002014 **
## Sub_region 0 21649.6 7216.5 3 332.02 4.2746 0.005584 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ Year + Sub_region + (1 | Month)pCO2_mc<-lmer(pCO2_uatm ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(pCO2_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3588.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8746 -0.5794 -0.1050 0.5867 4.2643
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 2567 50.66
## Residual 1688 41.09
## Number of obs: 348, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7606.989 2580.295 338.502 -2.948 0.00342 **
## Year 3.979 1.279 338.483 3.112 0.00201 **
## Sub_regionUK -11.913 7.130 332.019 -1.671 0.09570 .
## Sub_regionMK -12.236 6.310 331.987 -1.939 0.05331 .
## Sub_regionLK 5.362 6.297 332.030 0.851 0.39514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.012 0.011
## Sub_reginMK -0.002 0.000 0.530
## Sub_reginLK -0.018 0.017 0.532 0.601 anova(pCO2_mc) plot(pCO2_mc)
qqnorm(resid(pCO2_mc))
acf(resid(pCO2_mc), main="acf(resid(pCO2 Mid Channel))") 
pCO2_mc.emmc<-emmeans(pCO2_mc, ~Sub_region+Year)
pCO2_mc.emmc<-multcomp::cld(pCO2_mc.emmc)
pCO2_mc.emmc<-pCO2_mc.emmc[order(
pCO2_mc.emmc$Sub_region),]
pCO2_mc.emmc #write.csv(pCO2_mc.emmc, "FLK_results/Model_2_LTpCO2_POST.csv")# Predict values
Mc_pCO2_Pred <- predict(pCO2_mc, newdata=Mc_Newdata, re.form=NA, level=0, asList = FALSE)
Mc_pCO2_Pred <-cbind(Mc_Newdata, Mc_pCO2_Pred)
Mc_pCO2_Pred$Date<-as.Date(as.character(Mc_pCO2_Pred$Year), format = "%Y")
# Model summary
slope<-round(
summary(pCO2_mc)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pCO2_uatm, na.rm =T)
ys <- max(Trends.midchanel$pCO2_uatm, na.rm =T)+50
# Plot raw data point and fitted model
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Mc_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(200,800),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.midchanel,
aes(y=pCO2_uatm, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_pCO2_Pred, aes(y = Mc_pCO2_Pred,
x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_pCO2_Modeled
Offshore
hist(Trends.offshore$pCO2_uatm)
shapiro.test(Trends.offshore$pCO2_uatm)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$pCO2_uatm
## W = 0.94091, p-value = 6.045e-12pCO2_of_0<-lmer(pCO2_uatm~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(pCO2_of_0)
anova(pCO2_of_0) ranova(pCO2_of_0) step(pCO2_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -2035.5 4092.9
## (1 | SiteID) 1 10 -2035.5 4090.9 0.00 1 0.9999
## (1 | Month) 0 9 -2178.9 4375.8 286.89 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 3276 1092 3 404.84 1.2925 0.27657
## Year 0 54912 54912 1 414.03 64.8508 8.678e-15 ***
## Sub_region 0 9100 3033 3 407.84 3.5822 0.01397 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ Year + Sub_region + (1 | Month)pCO2_of<-lmer(pCO2_uatm ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(pCO2_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 4083.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6834 -0.6495 -0.0985 0.5935 4.4101
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1413.8 37.6
## Residual 846.7 29.1
## Number of obs: 424, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.290e+04 1.655e+03 4.141e+02 -7.798 5.16e-14 ***
## Year 6.603e+00 8.199e-01 4.140e+02 8.053 8.68e-15 ***
## Sub_regionUK -3.165e+00 4.482e+00 4.078e+02 -0.706 0.481
## Sub_regionMK -7.104e+00 4.438e+00 4.078e+02 -1.601 0.110
## Sub_regionLK 4.680e+00 4.220e+00 4.079e+02 1.109 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.004 0.002
## Sub_reginMK -0.005 0.003 0.591
## Sub_reginLK -0.014 0.012 0.623 0.630 anova(pCO2_of) plot(pCO2_of)
qqnorm(resid(pCO2_of))
acf(resid(Omega_in), main="acf(resid(pCO2 Offshore))") 
pCO2_of.emmc<-emmeans(pCO2_of, ~Sub_region)
pCO2_of.emmc<-multcomp::cld(pCO2_of.emmc)
pCO2_of.emmc<-pCO2_of.emmc[order(pCO2_of.emmc$Sub_region),]
pCO2_of.emmc #write.csv(pCO2_of.emmc, "FLK_results/Model_3_LTpCO2_POST.csv")# Predict values
Of_pCO2_Pred <- predict(pCO2_of, newdata=Of_Newdata, re.form=NA, level=0, asList = FALSE)
Of_pCO2_Pred <-cbind(Of_Newdata, Of_pCO2_Pred)
Of_pCO2_Pred$Date<-as.Date(as.character(Of_pCO2_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pCO2_of)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
ys <- max(Trends.offshore$pCO2_uatm, na.rm =T) -15
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(300,605),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.offshore,
aes(y=pCO2_uatm, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_pCO2_Pred, aes(y = Of_pCO2_Pred,
x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_pCO2_Modeled
Oceanic
hist(Trends.ocean$pCO2_uatm)
shapiro.test(Trends.ocean$pCO2_uatm)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$pCO2_uatm
## W = 0.9089, p-value = 1.793e-08pCO2_oc_0<-lmer(pCO2_uatm ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(pCO2_oc_0)
anova(pCO2_oc_0) ranova(pCO2_oc_0) step(pCO2_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -763.72 1545.4
## (1 | SiteID) 1 8 -763.72 1543.4 0.000 1 1
## (1 | Month) 0 7 -793.09 1600.2 58.747 1 1.793e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 77.9 39.0 2 143.41 0.0503 0.9510
## Sub_region 2 1222.5 611.3 2 145.31 0.7992 0.4517
## Year 0 26011.6 26011.6 1 155.26 34.0692 3.005e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ Year + (1 | Month)pCO2_oc<-lmer(pCO2_uatm ~ Year + (1 | Month),
data = Trends.ocean)
summary(pCO2_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1546.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3192 -0.6292 -0.1467 0.5901 2.8224
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 773.6 27.81
## Residual 763.5 27.63
## Number of obs: 161, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -14025.177 2474.792 155.267 -5.667 6.87e-08 ***
## Year 7.158 1.226 155.259 5.837 3.01e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(pCO2_oc) plot(pCO2_oc)
qqnorm(resid(pCO2_oc))
acf(resid(pCO2_oc), main="acf(resid(pCO2 open))")
pCO2_oc.emmc<-emmeans(pCO2_oc, ~Year)
pCO2_oc.emmc<-multcomp::cld(pCO2_oc.emmc)
#pCO2_oc.emmc<-pCO2_oc.emmc[order(pCO2_oc.emmc$Sub_region),]
pCO2_oc.emmc# Predict values
Oc_pCO2_Pred <- predict(pCO2_oc, newdata=Oc_Newdata,
re.form=NA, level=0, asList = FALSE)
Oc_pCO2_Pred <-cbind(Oc_Newdata, Oc_pCO2_Pred)
Oc_pCO2_Pred$Date<-as.Date(as.character(Oc_pCO2_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pCO2_oc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
ys <- max(Trends.offshore$pCO2_uatm, na.rm =T)-15
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(300,605),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.ocean,
aes(y=pCO2_uatm, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_pCO2_Pred, aes(y = Oc_pCO2_Pred, x=Date),
colour = "black", linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_pCO2_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.pCO2_in_0 <- data.frame (anova(pCO2_in_0))
av.pCO2_mc_0 <- data.frame (anova(pCO2_mc_0) )
av.pCO2_of_0 <- data.frame (anova(pCO2_of_0))
av.pCO2_oc_0 <- data.frame (anova(pCO2_oc_0))
# Putting all into one dataframe/table
anova_pH <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.pCO2_in_0, av.pCO2_mc_0,
av.pCO2_of_0, av.pCO2_oc_0)))
colnames(anova_pH) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_pH) <- NULL
# create HTML table using kableExtra
anova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pCO2 inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pCO2 mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pCO2 offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pCO2 ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| pCO2 inshore | ||||||
| Year | 3827.77 | 3827.77 | 1 | 353.59 | 0.94 | 0.33 |
| Sub_region | 11443.07 | 3814.36 | 3 | 347.42 | 0.93 | 0.42 |
| Year:Sub_region | 11398.40 | 3799.47 | 3 | 347.42 | 0.93 | 0.43 |
| pCO2 mid-channel | ||||||
| Year | 17990.76 | 17990.76 | 1 | 330.00 | 10.76 | 0.00 |
| Sub_region | 6574.85 | 2191.62 | 3 | 323.66 | 1.31 | 0.27 |
| Year:Sub_region | 6555.91 | 2185.30 | 3 | 323.66 | 1.31 | 0.27 |
| pCO2 offshore | ||||||
| Year | 56531.62 | 56531.62 | 1 | 410.81 | 66.90 | 0.00 |
| Sub_region | 3285.48 | 1095.16 | 3 | 404.82 | 1.30 | 0.28 |
| Year:Sub_region | 3276.20 | 1092.07 | 3 | 404.82 | 1.29 | 0.28 |
| pCO2 ocean | ||||||
| Year | 23313.90 | 23313.90 | 1 | 150.81 | 30.07 | 0.00 |
| Sub_region | 78.50 | 39.25 | 2 | 143.45 | 0.05 | 0.95 |
| Year:Sub_region | 77.93 | 38.97 | 2 | 143.45 | 0.05 | 0.95 |
# R-ANOVAs
rav.pCO2_in_0 <- data.frame (ranova(pCO2_in_0))
rav.pCO2_mc_0 <- data.frame (ranova(pCO2_mc_0) )
rav.pCO2_of_0 <- data.frame (ranova(pCO2_of_0))
rav.pCO2_oc_0 <- data.frame (ranova(pCO2_oc_0))
# Putting all into one dataframe/table
ranova_pCO2 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.pCO2_in_0, rav.pCO2_mc_0,
rav.pCO2_of_0, rav.pCO2_oc_0)))
colnames(ranova_pCO2) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_pCO2) <- NULL
# create HTML table using kableExtra
ranova_pCO2 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| <none> | 11 | -2081.86 | 4185.72 | |||
| (1 | Month) | 10 | -2239.12 | 4498.23 | 314.51 | 1 | 0.00 |
| (1 | SiteID) | 10 | -2084.83 | 4189.66 | 5.95 | 1 | 0.01 |
| Omega mid-channel | ||||||
| <none> | 11 | -1786.27 | 3594.53 | |||
| (1 | Month) | 10 | -1908.39 | 3836.78 | 244.25 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1786.45 | 3592.90 | 0.36 | 1 | 0.55 |
| Omega offshore | ||||||
| <none> | 11 | -2035.47 | 4092.93 | |||
| (1 | Month) | 10 | -2178.91 | 4377.82 | 286.89 | 1 | 0.00 |
| (1 | SiteID) | 10 | -2035.47 | 4090.93 | 0.00 | 1 | 1.00 |
| Omega ocean | ||||||
| <none> | 9 | -763.72 | 1545.43 | |||
| (1 | Month) | 8 | -793.09 | 1602.18 | 58.75 | 1 | 0.00 |
| (1 | SiteID) | 8 | -763.72 | 1543.43 | 0.00 | 1 | 1.00 |
pCO2_models_0<-list("Inshore" = pCO2_in_0,
"Mid channel" = pCO2_mc_0,
"Offshore" = pCO2_of_0,
"Ocean" = pCO2_oc_0)
modelsummary(pCO2_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pCO model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 3.785 | 5.358* | 7.884*** | 7.146** |
| (3.814) | (2.472) | (1.747) | (2.554) | |
| Intercept | −7198.143 | −10388.175* | −15489.367*** | −14002.750** |
| (7696.751) | (4988.665) | (3524.582) | (5153.529) | |
| Upper Keys | 3479.625 | −1169.721 | 2812.235 | |
| (9952.365) | (6981.992) | (4442.947) | ||
| Middle Keys | −899.345 | 721.299 | −512.538 | −720.552 |
| (9648.005) | (6194.920) | (4403.595) | (6031.071) | |
| Lower Keys | 12648.581 | 9366.041 | 6102.617 | 816.986 |
| (9705.503) | (6249.496) | (4195.522) | (6074.570) | |
| Num.Obs. | 373 | 348 | 424 | 161 |
| R2 Marg. | 0.024 | 0.033 | 0.198 | 0.225 |
| R2 Cond. | 0.665 | 0.620 | ||
| AIC | 4185.7 | 3594.5 | 4092.9 | 1545.4 |
| BIC | 4228.9 | 3636.9 | 4137.5 | 1573.2 |
| ICC | 0.7 | 0.6 | ||
| RMSE | 61.84 | 39.68 | 28.42 | 26.41 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Factors removed models
pCO2_models<-list("Inshore" = pCO2_in,
"Mid channel" = pCO2_mc,
"Offshore" = pCO2_of,
"Ocean" = pCO2_oc)
modelsummary(pCO2_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pCO2 models outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 3.979** | 6.603*** | 7.158*** | |
| (1.279) | (0.820) | (1.226) | ||
| Intercept | 427.280*** | −7606.989** | −12902.943*** | −14025.177*** |
| (25.822) | (2580.295) | (1654.695) | (2474.792) | |
| Upper Keys | −11.913+ | −3.165 | ||
| (7.130) | (4.482) | |||
| Middle Keys | −12.236+ | −7.103 | ||
| (6.310) | (4.438) | |||
| Lower Keys | 5.362 | 4.680 | ||
| (6.297) | (4.219) | |||
| Num.Obs. | 373 | 348 | 424 | 161 |
| R2 Marg. | 0.000 | 0.029 | 0.082 | 0.123 |
| R2 Cond. | 0.657 | 0.615 | 0.656 | 0.565 |
| AIC | 4220.8 | 3602.4 | 4097.8 | 1554.9 |
| BIC | 4236.5 | 3629.4 | 4126.2 | 1567.2 |
| ICC | 0.7 | 0.6 | 0.6 | 0.5 |
| RMSE | 62.18 | 40.15 | 28.55 | 26.57 |
| + p |
#modelsummary(pCO2_models, estimate = "p.value")Trends_pCO2<-ggarrange(In_pCO2_Modeled+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Mc_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
Of_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
Oc_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
labels = c("A"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_pCO2
pH-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$pH_calculated
## W = 0.97877, p-value = 6.115e-13
## Model with all data
pH_all_o<-lmer(pH_calculated ~ Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(pH_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 2114.1 -4162.2
## (1 | SiteID) 1 32 2113.0 -4162.1 2.14 1 0.1432
## (1 | Month) 0 31 1697.5 -3333.0 831.12 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value
## Year:Zone:Sub_region 1 0.002341 0.0002926 8 1264.9 0.1612
## Year:Sub_region 2 0.010214 0.0034048 3 1273.2 1.8859
## Year:Zone 0 0.027362 0.0091205 3 1276.0 5.0413
## Zone:Sub_region 0 0.057144 0.0071431 8 1276.0 3.9483
## Pr(>F)
## Year:Zone:Sub_region 0.9956439
## Year:Sub_region 0.1300852
## Year:Zone 0.0017821 **
## Zone:Sub_region 0.0001255 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone + Zone:Sub_regionpH_all_o_1<-lmer(pH_calculated ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
pH_all_o_3<-lmer(pH_calculated ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(pH_all_o, pH_all_o_1, pH_all_o_3) summary(pH_all_o_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: -4327.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0984 -0.5453 0.0507 0.5325 4.6588
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.002210 0.04701
## Residual 0.001809 0.04253
## Number of obs: 1306, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.757e+00 2.315e+00 1.280e+03 3.351 0.000829
## Year 1.329e-04 1.147e-03 1.280e+03 0.116 0.907769
## ZoneMid channel 5.458e+00 3.153e+00 1.276e+03 1.731 0.083704
## ZoneOffshore 9.877e+00 3.004e+00 1.276e+03 3.288 0.001036
## ZoneOceanic 1.269e+01 3.963e+00 1.276e+03 3.202 0.001399
## Sub_regionUK 2.421e-02 6.662e-03 1.276e+03 3.634 0.000291
## Sub_regionMK 1.660e-02 6.524e-03 1.276e+03 2.545 0.011051
## Sub_regionLK -2.231e-02 6.488e-03 1.276e+03 -3.438 0.000605
## Year:ZoneMid channel -2.700e-03 1.563e-03 1.276e+03 -1.728 0.084206
## Year:ZoneOffshore -4.889e-03 1.489e-03 1.276e+03 -3.285 0.001048
## Year:ZoneOceanic -6.273e-03 1.964e-03 1.276e+03 -3.194 0.001439
## ZoneMid channel:Sub_regionUK -1.280e-02 9.936e-03 1.276e+03 -1.288 0.197859
## ZoneOffshore:Sub_regionUK -2.099e-02 9.343e-03 1.276e+03 -2.247 0.024822
## ZoneOceanic:Sub_regionUK -3.802e-02 1.126e-02 1.276e+03 -3.377 0.000755
## ZoneMid channel:Sub_regionMK -8.770e-03 9.224e-03 1.276e+03 -0.951 0.341895
## ZoneOffshore:Sub_regionMK -1.245e-02 9.196e-03 1.276e+03 -1.354 0.175919
## ZoneOceanic:Sub_regionMK -3.365e-02 9.478e-03 1.276e+03 -3.550 0.000398
## ZoneMid channel:Sub_regionLK 1.285e-02 9.180e-03 1.276e+03 1.400 0.161896
## ZoneOffshore:Sub_regionLK 1.862e-02 8.934e-03 1.276e+03 2.084 0.037377
##
## (Intercept) ***
## Year
## ZoneMid channel .
## ZoneOffshore **
## ZoneOceanic **
## Sub_regionUK ***
## Sub_regionMK *
## Sub_regionLK ***
## Year:ZoneMid channel .
## Year:ZoneOffshore **
## Year:ZoneOceanic **
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK *
## ZoneOceanic:Sub_regionUK ***
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK
## ZoneOceanic:Sub_regionMK ***
## ZoneMid channel:Sub_regionLK
## ZoneOffshore:Sub_regionLK *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(pH_all_o_1) ranova(pH_all_o_1) ranef(pH_all_o_1)## $Month
## (Intercept)
## Apr 0.054975419
## May 0.049160922
## Jun -0.013467739
## Jul -0.055830887
## Aug -0.032931185
## Sep -0.065502539
## Oct -0.034828359
## Nov -0.040939492
## Dec 0.008610227
## Jan 0.016861511
## Feb 0.068036630
## Mar 0.045855491
##
## with conditional variances for "Month" fixef(pH_all_o_1)## (Intercept) Year
## 7.7571304554 0.0001329224
## ZoneMid channel ZoneOffshore
## 5.4580497593 9.8770992079
## ZoneOceanic Sub_regionUK
## 12.6907155256 0.0242072024
## Sub_regionMK Sub_regionLK
## 0.0166026830 -0.0223062905
## Year:ZoneMid channel Year:ZoneOffshore
## -0.0027003365 -0.0048895011
## Year:ZoneOceanic ZoneMid channel:Sub_regionUK
## -0.0062730790 -0.0128011155
## ZoneOffshore:Sub_regionUK ZoneOceanic:Sub_regionUK
## -0.0209924076 -0.0380242862
## ZoneMid channel:Sub_regionMK ZoneOffshore:Sub_regionMK
## -0.0087702108 -0.0124536166
## ZoneOceanic:Sub_regionMK ZoneMid channel:Sub_regionLK
## -0.0336516270 0.0128475157
## ZoneOffshore:Sub_regionLK
## 0.0186174248 coef(pH_all_o_1)$Month coef(pH_all_o_1)$Zone## NULL m.lst <- lstrends(pH_all_o_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore 0.000133 0.00115 1280 -0.00212 0.002384
## Mid channel -0.002567 0.00118 1279 -0.00489 -0.000247
## Offshore -0.004757 0.00108 1280 -0.00688 -0.002635
## Oceanic -0.006140 0.00167 1278 -0.00942 -0.002856
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel 0.00270 0.00156 1276 1.728 0.3094
## Inshore - Offshore 0.00489 0.00149 1276 3.285 0.0058
## Inshore - Oceanic 0.00627 0.00196 1276 3.194 0.0078
## Mid channel - Offshore 0.00219 0.00152 1276 1.443 0.4724
## Mid channel - Oceanic 0.00357 0.00199 1276 1.800 0.2739
## Offshore - Oceanic 0.00138 0.00193 1276 0.718 0.8901
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimates # Create a new data frame for independent variables
Newdata<-expand.grid(Zone=unique(Trends.data$Zone),
Sub_region=unique(Trends.data$Sub_region),
Year=seq((min(Trends.data$Year)-0), (max(Trends.data$Year)+1)))
# Predict values
pH_Pred <- predict(pH_all_o_1, newdata=Newdata,
re.form=NA, level=0, asList = FALSE)
pH_Pred <-cbind(Newdata, pH_Pred)
pH_Pred$Date<-as.Date(as.character(pH_Pred$Year), format = "%Y")
pH_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.data,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5,shape=21)+
geom_line(data=pH_Pred, aes(y = pH_Pred, x=Date, colour = Sub_region),
linetype=2, size=1) #+
#annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
# label =paste0(as.character(plot_label, plot_label2, sep = " ")),
# size=4)
pH_Modeled
Inshore
# Inshore pH
hist(Trends.inshore$pH_calculated)
shapiro.test(Trends.inshore$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$pH_calculated
## W = 0.98963, p-value = 0.009646pH_in_0<-lmer(pH_calculated~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(pH_in_0)
anova(pH_in_0) ranova(pH_in_0) step(pH_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 518.84 -1015.68
## (1 | Month) 0 10 343.63 -667.26 350.42 1 < 2e-16 ***
## (1 | SiteID) 0 10 516.35 -1012.71 4.97 1 0.02574 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.007655 0.0025518 3 347.54 0.9699 0.40703
## Year 2 0.000441 0.0004414 1 356.04 0.1677 0.68239
## Sub_region 0 0.041536 0.0138455 3 7.03 5.2723 0.03229 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID)pH_in<-lmer(pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID),
data=Trends.inshore)
summary(pH_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: -1074.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8173 -0.6283 -0.0120 0.5979 3.7926
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0057954 0.07613
## SiteID (Intercept) 0.0001307 0.01143
## Residual 0.0026261 0.05125
## Number of obs: 373, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.02708 0.02420 14.58618 331.764 <2e-16 ***
## Sub_regionUK 0.02175 0.01317 7.03024 1.651 0.143
## Sub_regionMK 0.01695 0.01307 6.82486 1.297 0.237
## Sub_regionLK -0.02075 0.01305 6.77579 -1.590 0.157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.318
## Sub_reginMK -0.321 0.589
## Sub_reginLK -0.322 0.590 0.595 anova(pH_in) plot(pH_in)
qqnorm(resid(pH_in))
acf(resid(pH_in), main="acf(resid(pH Inshore))") 
pH_in.emmc<-emmeans(pH_in, ~Sub_region)
pH_in.emmc<-multcomp::cld(pH_in.emmc)
pH_in.emmc<-pH_in.emmc[order(pH_in.emmc$Sub_region),]
pH_in.emmc#write.csv(pH_in.emmc, "FLK_results/Model_5_LTpH_POST.csv")# Predict values
In_pH_Pred <- predict(pH_in, newdata=In_Newdata,
re.form=NA, level=0, asList = FALSE)
In_pH_Pred <-cbind(In_Newdata, In_pH_Pred)
In_pH_Pred$Date<-as.Date(as.character(In_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
# slope<-round(
# summary(pH_in)$coefficients["Year", "Estimate"], digits = 3)
# xs <- as.Date(as.character("2018-11-20"))
# ys <- max(Trends.inshore$pH_calculated, na.rm =T)
# plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
# plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
In_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5,shape=21)+
geom_line(data=In_pH_Pred, aes(y = In_pH_Pred, x=Date, colour = Sub_region),
linetype=2, size=1) #+
#annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
# label =paste0(as.character(plot_label, plot_label2, sep = " ")),
# size=4)
In_pH_Modeled
Mid channel
hist(Trends.midchanel$pH_calculated)
shapiro.test(Trends.midchanel$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$pH_calculated
## W = 0.98753, p-value = 0.004369pH_mc_0<-lmer(pH_calculated~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(pH_mc_0)
anova(pH_mc_0) ranova(pH_mc_0) step(pH_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 621.59 -1221.18
## (1 | SiteID) 1 10 621.36 -1222.72 0.456 1 0.4997
## (1 | Month) 0 9 484.04 -950.08 274.647 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.0041921 0.0013974 3 328.97 1.1815 0.3167596
## Year 0 0.0056820 0.0056820 1 337.72 4.7964 0.0292023
## Sub_region 0 0.0237637 0.0079212 3 332.00 6.6866 0.0002148
##
## Year:Sub_region
## Year *
## Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Year + Sub_region + (1 | Month)pH_mc<-lmer(pH_calculated ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(pH_mc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: -1270
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5029 -0.6297 0.0278 0.5480 4.0468
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.002128 0.04613
## Residual 0.001185 0.03442
## Number of obs: 348, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 12.771183 2.163374 337.738692 5.903 8.67e-09 ***
## Year -0.002348 0.001072 337.716529 -2.190 0.0292 *
## Sub_regionUK 0.011699 0.005973 332.002681 1.959 0.0510 .
## Sub_regionMK 0.007645 0.005285 331.975599 1.447 0.1490
## Sub_regionLK -0.009538 0.005275 332.012556 -1.808 0.0715 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.012 0.011
## Sub_reginMK -0.002 0.000 0.530
## Sub_reginLK -0.018 0.017 0.532 0.601 anova(pH_mc) plot(pH_mc)
qqnorm(resid(pH_mc))
acf(resid(pH_mc), main="acf(resid(pH Mid Channel))") 
pH_mc.emmc<-emmeans(pH_mc, ~Sub_region)
pH_mc.emmc<-multcomp::cld(pH_mc.emmc)
pH_mc.emmc<-pH_mc.emmc[order(pH_mc.emmc$Sub_region),]
pH_mc.emmc #write.csv(pH_mc.emmc, "FLK_results/Model_6_LTpH_POST.csv")# Predict values
Mc_pH_Pred <- predict(pH_mc, newdata=Mc_Newdata,
re.form=NA, level=0, asList = FALSE)
Mc_pH_Pred <-cbind(Mc_Newdata, Mc_pH_Pred)
Mc_pH_Pred$Date<-as.Date(as.character(Mc_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_mc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.midchanel$pH_calculated, na.rm =T)+0.03
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Mc_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
breaks = seq(0, 10, 0.1),
limits=c(7.80, 8.28),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.midchanel,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_pH_Pred, aes(y = Mc_pH_Pred, x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_pH_Modeled
Offshore
hist(Trends.offshore$pH_calculated)
shapiro.test(Trends.offshore$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$pH_calculated
## W = 0.97503, p-value = 1.157e-06pH_of_0<-lmer(pH_calculated~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(pH_of_0)
anova(pH_of_0) ranova(pH_of_0) step(pH_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 929.90 -1837.8
## (1 | SiteID) 1 10 929.90 -1839.8 0.00 1 1
## (1 | Month) 0 9 762.18 -1506.3 335.44 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.0025882 0.0008627 3 404.84 1.5961 0.18976
## Year 0 0.0270864 0.0270864 1 413.06 49.8921 6.937e-12
## Sub_region 0 0.0057365 0.0019122 3 407.86 3.5222 0.01514
##
## Year:Sub_region
## Year ***
## Sub_region *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Year + Sub_region + (1 | Month)pH_of<-lmer(pH_calculated ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(pH_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: -1888.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0380 -0.6374 0.0984 0.6899 2.8145
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0011248 0.03354
## Residual 0.0005429 0.02330
## Number of obs: 424, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.740e+01 1.326e+00 4.131e+02 13.122 < 2e-16 ***
## Year -4.641e-03 6.571e-04 4.131e+02 -7.063 6.94e-12 ***
## Sub_regionUK 1.905e-03 3.589e-03 4.078e+02 0.531 0.596
## Sub_regionMK 4.307e-03 3.553e-03 4.078e+02 1.212 0.226
## Sub_regionLK -4.893e-03 3.379e-03 4.079e+02 -1.448 0.148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.004 0.002
## Sub_reginMK -0.005 0.003 0.591
## Sub_reginLK -0.014 0.012 0.623 0.630 anova(pH_of) plot(pH_of)
qqnorm(resid(pH_of))
acf(resid(pH_of), main="acf(resid(pH Offshore))")
pH_of.emmc<-emmeans(pH_of, ~Sub_region)
pH_of.emmc<-multcomp::cld(pH_of.emmc)
pH_of.emmc<-pH_of.emmc[order(pH_of.emmc$Sub_region),]
pH_of.emmc #write.csv(pH_of.emmc, "FLK_results/Model_7_LTpH_POST.csv")# Predict values
Of_pH_Pred <- predict(pH_of, newdata=Of_Newdata,
re.form=NA, level=0, asList = FALSE)
Of_pH_Pred <-cbind(Of_Newdata, Of_pH_Pred)
Of_pH_Pred$Date<-as.Date(as.character(Of_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_of)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.offshore$pH_calculated, na.rm =T)+0.08
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.88, 8.2),
# limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0, 0.01)) +
geom_point(data=Trends.offshore,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_pH_Pred, aes(y = Of_pH_Pred, x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_pH_Modeled
Oceanic
hist(Trends.ocean$pH_calculated)
shapiro.test(Trends.ocean$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$pH_calculated
## W = 0.94851, p-value = 1.265e-05pH_oc_0<-lmer(pH_calculated ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(pH_oc_0)
anova(pH_oc_0) ranova(pH_oc_0) step(pH_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 340.84 -663.69
## (1 | SiteID) 1 8 340.84 -665.69 0.000 1 1
## (1 | Month) 0 7 306.65 -599.30 68.385 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.0000317 0.0000158 2 143.50 0.0320 0.9686
## Sub_region 2 0.0009016 0.0004508 2 145.38 0.9225 0.3998
## Year 0 0.0132501 0.0132501 1 154.48 27.1193 6.021e-07
##
## Year:Sub_region
## Sub_region
## Year ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Year + (1 | Month)pH_oc<-lmer(pH_calculated ~ Year + (1 | Month),
data=Trends.ocean)
summary(pH_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: -719.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6473 -0.6723 0.1239 0.6369 2.4346
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0005838 0.02416
## Residual 0.0004886 0.02210
## Number of obs: 161, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.837e+01 1.984e+00 1.545e+02 9.257 < 2e-16 ***
## Year -5.120e-03 9.832e-04 1.545e+02 -5.208 6.02e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(pH_oc) plot(pH_oc)
qqnorm(resid(pH_oc))
acf(resid(pH_oc), main="acf(resid(pH ocean))")
pH_oc.emmc<-emmeans(pH_oc, ~Year)
pH_oc.emmc<-multcomp::cld(pH_oc.emmc)
#pH_oc.emmc<-pH_oc.emmc[order(pH_oc.emmc$Sub_region),]
pH_oc.emmc# Predict values
Oc_pH_Pred <- predict(pH_oc, newdata=Oc_Newdata,
re.form=NA, level=0, asList = FALSE)
Oc_pH_Pred <-cbind(Oc_Newdata, Oc_pH_Pred)
Oc_pH_Pred$Date<-as.Date(as.character(Oc_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_oc)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.offshore$pH_calculated, na.rm =T)+0.08
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.88, 8.2),
# limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0, 0.01)) +
geom_point(data=Trends.ocean,
aes(y=pH_calculated, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_pH_Pred, aes(y = Oc_pH_Pred, x=Date),
linetype=1, size=1, colour = "black") +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_pH_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.pH_in_0 <- data.frame (anova(pH_in_0))
av.pH_mc_0 <- data.frame (anova(pH_mc_0) )
av.pH_of_0 <- data.frame (anova(pH_of_0))
av.pH_oc_0 <- data.frame (anova(pH_oc_0))
# Putting all into one dataframe/table
anova_pH <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.pH_in_0, av.pH_mc_0,
av.pH_of_0, av.pH_oc_0)))
colnames(anova_pH) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_pH) <- NULL
# create HTML table using kableExtra
anova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pH inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pH mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pH offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pH ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| pH inshore | ||||||
| Year | 0.00 | 0.00 | 1 | 352.91 | 0.31 | 0.58 |
| Sub_region | 0.01 | 0.00 | 3 | 347.54 | 0.97 | 0.40 |
| Year:Sub_region | 0.01 | 0.00 | 3 | 347.54 | 0.97 | 0.41 |
| pH mid-channel | ||||||
| Year | 0.01 | 0.01 | 1 | 329.18 | 5.72 | 0.02 |
| Sub_region | 0.00 | 0.00 | 3 | 323.55 | 1.21 | 0.31 |
| Year:Sub_region | 0.00 | 0.00 | 3 | 323.56 | 1.20 | 0.31 |
| pH offshore | ||||||
| Year | 0.03 | 0.03 | 1 | 409.83 | 52.89 | 0.00 |
| Sub_region | 0.00 | 0.00 | 3 | 404.84 | 1.60 | 0.19 |
| Year:Sub_region | 0.00 | 0.00 | 3 | 404.84 | 1.60 | 0.19 |
| pH ocean | ||||||
| Year | 0.01 | 0.01 | 1 | 150.06 | 23.63 | 0.00 |
| Sub_region | 0.00 | 0.00 | 2 | 143.49 | 0.03 | 0.97 |
| Year:Sub_region | 0.00 | 0.00 | 2 | 143.49 | 0.03 | 0.97 |
# R-ANOVAs
rav.pH_in_0 <- data.frame (ranova(pH_in_0))
rav.pH_mc_0 <- data.frame (ranova(pH_mc_0) )
rav.pH_of_0 <- data.frame (ranova(pH_of_0))
rav.pH_oc_0 <- data.frame (ranova(pH_oc_0))
# Putting all into one dataframe/table
ranova_pH <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.pH_in_0, rav.pH_mc_0,
rav.pH_of_0, rav.pH_oc_0)))
colnames(ranova_pH) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_pH) <- NULL
# create HTML table using kableExtra
ranova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pH inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pH mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pH offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pH ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| pH inshore | ||||||
| <none> | 11 | 518.84 | -1015.68 | |||
| (1 | Month) | 10 | 343.63 | -667.26 | 350.42 | 1 | 0.00 |
| (1 | SiteID) | 10 | 516.35 | -1012.71 | 4.97 | 1 | 0.03 |
| pH mid-channel | ||||||
| <none> | 11 | 621.59 | -1221.18 | |||
| (1 | Month) | 10 | 484.04 | -948.08 | 275.10 | 1 | 0.00 |
| (1 | SiteID) | 10 | 621.36 | -1222.72 | 0.46 | 1 | 0.50 |
| pH offshore | ||||||
| <none> | 11 | 929.90 | -1837.79 | |||
| (1 | Month) | 10 | 762.18 | -1504.35 | 335.44 | 1 | 0.00 |
| (1 | SiteID) | 10 | 929.90 | -1839.79 | 0.00 | 1 | 1.00 |
| pH ocean | ||||||
| <none> | 9 | 340.84 | -663.69 | |||
| (1 | Month) | 8 | 306.65 | -597.30 | 68.39 | 1 | 0.00 |
| (1 | SiteID) | 8 | 340.84 | -665.69 | 0.00 | 1 | 1.00 |
pH_models_0<-list("Inshore" = pH_in_0,
"Mid channel" = pH_mc_0,
"Offshore" = pH_of_0,
"Ocean" = pH_oc_0)
modelsummary(pH_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pH model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.004 | −0.004+ | −0.006*** | −0.005* |
| (0.003) | (0.002) | (0.001) | (0.002) | |
| Intercept | 15.192* | 16.018*** | 20.093*** | 17.934*** |
| (6.182) | (4.180) | (2.820) | (4.123) | |
| Upper Keys | −3.229 | −0.282 | −2.967 | |
| (7.992) | (5.849) | (3.554) | ||
| Middle Keys | −5.943 | −2.258 | −0.158 | 0.921 |
| (7.747) | (5.189) | (3.522) | (4.822) | |
| Lower Keys | −12.466 | −8.421 | −5.795+ | 0.039 |
| (7.794) | (5.235) | (3.356) | (4.856) | |
| Num.Obs. | 373 | 348 | 424 | 161 |
| R2 Marg. | 0.036 | 0.030 | 0.167 | 0.191 |
| R2 Cond. | 0.706 | 0.657 | ||
| AIC | −1015.7 | −1221.2 | −1837.8 | −663.7 |
| BIC | −972.5 | −1178.8 | −1793.2 | −636.0 |
| ICC | 0.7 | 0.6 | ||
| RMSE | 0.05 | 0.03 | 0.02 | 0.02 |
| + p |
#modelsummary(pH_models, estimate = "p.value")Factors removed models
pH_models<-list("Inshore" = pH_in,
"Mid channel" = pH_mc,
"Offshore" = pH_of,
"Ocean" = pH_oc)
modelsummary(pH_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pH model outputs (factors dropped)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.002* | −0.005*** | −0.005*** | |
| (0.001) | (0.001) | (0.001) | ||
| Intercept | 8.027*** | 12.771*** | 17.400*** | 18.368*** |
| (0.024) | (2.163) | (1.326) | (1.984) | |
| Upper Keys | 0.022+ | 0.012+ | 0.002 | |
| (0.013) | (0.006) | (0.004) | ||
| Middle Keys | 0.017 | 0.008 | 0.004 | |
| (0.013) | (0.005) | (0.004) | ||
| Lower Keys | −0.021 | −0.010+ | −0.005 | |
| (0.013) | (0.005) | (0.003) | ||
| Num.Obs. | 373 | 348 | 424 | 161 |
| R2 Marg. | 0.034 | 0.027 | 0.058 | 0.094 |
| R2 Cond. | 0.703 | 0.652 | 0.693 | 0.587 |
| AIC | −1060.4 | −1256.0 | −1874.8 | −711.1 |
| BIC | −1033.0 | −1229.1 | −1846.4 | −698.8 |
| ICC | 0.7 | 0.6 | 0.7 | 0.5 |
| RMSE | 0.05 | 0.03 | 0.02 | 0.02 |
| + p |
#modelsummary(pH_models, estimate = "p.value")Trends_pH<-ggarrange(In_pH_Modeled+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Mc_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
Of_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
Oc_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
labels = c("B"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_pH
TA-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$TA_umol_kg
## W = 0.81138, p-value < 2.2e-16
Inshore
# Inshore TA
hist(Trends.inshore$TA_umol_kg)
shapiro.test(Trends.inshore$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$TA_umol_kg
## W = 0.97131, p-value = 9.403e-07TA_in_0<-lmer(TA_umol_kg~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(TA_in_0)
anova(TA_in_0) ranova(TA_in_0) step(TA_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -2111.8 4245.5
## (1 | SiteID) 1 10 -2112.4 4244.9 1.315 1 0.2515
## (1 | Month) 0 9 -2188.0 4394.0 151.123 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 23921 7974 3 356.06 1.6785 0.17127
## Year 2 15420 15420 1 369.35 3.2277 0.07322 .
## Sub_region 0 500931 166977 3 359.97 34.7828 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Sub_region + (1 | Month)TA_in<-lmer(TA_umol_kg ~ Sub_region + (1 | Month),
data=Trends.inshore)
summary(TA_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 4250.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5060 -0.5701 0.0374 0.6109 3.2811
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 3771 61.41
## Residual 4801 69.29
## Number of obs: 375, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2377.924 19.544 14.611 121.672 < 2e-16 ***
## Sub_regionUK 1.656 10.851 359.916 0.153 0.87876
## Sub_regionMK -31.623 10.639 359.921 -2.973 0.00315 **
## Sub_regionLK -87.269 10.552 359.997 -8.270 2.62e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.311
## Sub_reginMK -0.319 0.571
## Sub_reginLK -0.322 0.574 0.589 plot(TA_in)
qqnorm(resid(TA_in))
acf(resid(TA_in), main="acf(resid(TA Inshore))") 
TA_in.emmc<-emmeans(TA_in, ~Sub_region)
TA_in.emmc<-multcomp::cld(TA_in.emmc)
TA_in.emmc<-TA_in.emmc[order(TA_in.emmc$Sub_region),]
TA_in.emmcMid channel
hist(Trends.midchanel$TA_umol_kg)
shapiro.test(Trends.midchanel$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$TA_umol_kg
## W = 0.8639, p-value < 2.2e-16TA_mc_0<-lmer(TA_umol_kg~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(TA_mc_0)
anova(TA_mc_0) ranova(TA_mc_0) step(TA_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1817.1 3656.2
## (1 | Month) 0 10 -1849.4 3718.9 64.655 1 8.922e-16 ***
## (1 | SiteID) 0 10 -1818.7 3657.4 3.158 1 0.07558 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 6340.2 2113.4 3 325.60 1.0805 0.35744
## Year 2 3283.1 3283.1 1 337.74 1.6769 0.19622
## Sub_region 3 26280.6 8760.2 3 6.22 4.4754 0.05406 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ (1 | Month) + (1 | SiteID)TA_mc<-lmer(TA_umol_kg ~ (1 | Month) + (1|SiteID),
data = Trends.midchanel)
summary(TA_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ (1 | Month) + (1 | SiteID)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3682.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0700 -0.3976 0.1134 0.5259 3.4567
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 696.8 26.40
## SiteID (Intercept) 232.8 15.26
## Residual 1957.7 44.25
## Number of obs: 350, groups: Month, 12; SiteID, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2365.447 9.358 16.222 252.8 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(TA_mc)
qqnorm(resid(TA_mc))
acf(resid(TA_mc), main="acf(resid(TA Mid Channel))") 
Offshore
hist(Trends.offshore$TA_umol_kg)
shapiro.test(Trends.offshore$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$TA_umol_kg
## W = 0.88506, p-value < 2.2e-16TA_of_0<-lmer(TA_umol_kg~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(TA_of_0)
anova(TA_of_0) ranova(TA_of_0) step(TA_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1856.1 3734.2
## (1 | SiteID) 1 10 -1856.1 3732.2 0.000 1 1
## (1 | Month) 0 9 -1878.5 3775.1 44.909 1 2.064e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 154.6 51.53 3 408.87 0.1454 0.93261
## Year 0 1806.8 1806.84 1 400.97 5.1298 0.02405 *
## Sub_region 0 2996.0 998.68 3 412.25 2.8353 0.03792 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Year + Sub_region + (1 | Month)TA_of<-lmer(TA_umol_kg ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(TA_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3719
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5140 -0.3773 0.1021 0.5952 3.4228
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 58.97 7.679
## Residual 352.23 18.768
## Number of obs: 427, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.5577 1032.8949 400.9112 0.040 0.9679
## Year 1.1592 0.5118 400.9684 2.265 0.0241 *
## Sub_regionUK -0.5389 2.8906 411.7814 -0.186 0.8522
## Sub_regionMK -6.2459 2.8620 411.7525 -2.182 0.0296 *
## Sub_regionLK -5.2199 2.7133 412.6123 -1.924 0.0551 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.004 0.002
## Sub_reginMK -0.005 0.003 0.591
## Sub_reginLK -0.007 0.006 0.625 0.632 plot(TA_of)
qqnorm(resid(TA_of))
acf(resid(TA_of), main="acf(resid(TA Offshore))") 
TA_of.emmc<-emmeans(TA_of, ~Sub_region)
TA_of.emmc<-multcomp::cld(TA_of.emmc)
TA_of.emmc<-TA_of.emmc[order(TA_of.emmc$Sub_region),]
TA_of.emmcOceanic
hist(Trends.ocean$TA_umol_kg)
shapiro.test(Trends.ocean$TA_umol_kg)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$TA_umol_kg
## W = 0.9887, p-value = 0.2158TA_oc_0<-lmer(TA_umol_kg ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(TA_oc_0)
anova(TA_oc_0) ranova(TA_oc_0) step(TA_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -620.60 1259.2
## (1 | SiteID) 1 8 -620.60 1257.2 0.000 1 1.000000
## (1 | Month) 0 7 -626.34 1266.7 11.479 1 0.000704 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 36.60 18.30 2 147.47 0.1497 0.861116
## Sub_region 2 240.91 120.45 2 149.14 0.9962 0.371707
## Year 0 1185.68 1185.68 1 157.86 9.7917 0.002088 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Year + (1 | Month)TA_oc<-lmer(TA_umol_kg ~ (1 | Month),
data = Trends.ocean)
summary(TA_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1263.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7341 -0.5888 0.1002 0.6518 2.8046
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 21.01 4.584
## Residual 128.16 11.321
## Number of obs: 163, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2381.578 1.612 11.140 1478 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(TA_oc)
qqnorm(resid(TA_oc))
acf(resid(TA_oc), main="acf(resid(TA_Ocean))")
# TA_oc.emmc<-emmeans(TA_oc, ~Sub_region)
# TA_oc.emmc<-multcomp::cld(TA_oc.emmc)
# TA_oc.emmc<-TA_oc.emmc[order(TA_oc.emmc$Sub_region),]
# TA_oc.emmcModels summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.TA_in_0 <- data.frame (anova(TA_in_0))
av.TA_mc_0 <- data.frame (anova(TA_mc_0) )
av.TA_of_0 <- data.frame (anova(TA_of_0))
av.TA_oc_0 <- data.frame (anova(TA_oc_0))
# Putting all into one dataframe/table
anova_TA_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.TA_in_0, av.TA_mc_0,
av.TA_of_0, av.TA_oc_0)))
colnames(anova_TA_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_TA_0) <- NULL
# create HTML table using kableExtra
anova_TA_0 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "TA mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "TA offshore", 7, 9) %>% # groups rows with label
pack_rows(., "TA ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| TA inshore | ||||||
| Year | 12144.36 | 12144.36 | 1 | 360.75 | 2.59 | 0.11 |
| Sub_region | 24071.88 | 8023.96 | 3 | 350.04 | 1.71 | 0.16 |
| Year:Sub_region | 24057.93 | 8019.31 | 3 | 350.04 | 1.71 | 0.16 |
| TA mid-channel | ||||||
| Year | 2234.06 | 2234.06 | 1 | 335.44 | 1.14 | 0.29 |
| Sub_region | 6343.28 | 2114.43 | 3 | 325.60 | 1.08 | 0.36 |
| Year:Sub_region | 6340.22 | 2113.41 | 3 | 325.60 | 1.08 | 0.36 |
| TA offshore | ||||||
| Year | 1575.10 | 1575.10 | 1 | 401.06 | 4.44 | 0.04 |
| Sub_region | 155.57 | 51.86 | 3 | 408.83 | 0.15 | 0.93 |
| Year:Sub_region | 154.60 | 51.53 | 3 | 408.83 | 0.15 | 0.93 |
| TA ocean | ||||||
| Year | 1082.81 | 1082.81 | 1 | 155.65 | 8.86 | 0.00 |
| Sub_region | 36.41 | 18.21 | 2 | 147.46 | 0.15 | 0.86 |
| Year:Sub_region | 36.60 | 18.30 | 2 | 147.46 | 0.15 | 0.86 |
# R-ANOVAs
rav.TA_in_0 <- data.frame (ranova(TA_in_0))
rav.TA_mc_0 <- data.frame (ranova(TA_mc_0) )
rav.TA_of_0 <- data.frame (ranova(TA_of_0))
rav.TA_oc_0 <- data.frame (ranova(TA_oc_0))
# Putting all into one dataframe/table
ranova_TA_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.TA_in_0, rav.TA_mc_0,
rav.TA_of_0, rav.TA_oc_0)))
colnames(ranova_TA_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_TA_0) <- NULL
# create HTML table using kableExtra
ranova_TA_0 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "TA mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "TA offshore", 7, 9) %>% # groups rows with label
pack_rows(., "TA ocean", 10, 12)| npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| TA inshore | ||||||
| <none> | 11 | -2111.77 | 4245.54 | |||
| (1 | Month) | 10 | -2187.98 | 4395.97 | 152.43 | 1 | 0.00 |
| (1 | SiteID) | 10 | -2112.43 | 4244.86 | 1.32 | 1 | 0.25 |
| TA mid-channel | ||||||
| <none> | 11 | -1817.11 | 3656.22 | |||
| (1 | Month) | 10 | -1849.44 | 3718.87 | 64.66 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1818.69 | 3657.37 | 3.16 | 1 | 0.08 |
| TA offshore | ||||||
| <none> | 11 | -1856.08 | 3734.16 | |||
| (1 | Month) | 10 | -1878.53 | 3777.07 | 44.91 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1856.08 | 3732.16 | 0.00 | 1 | 1.00 |
| TA ocean | ||||||
| <none> | 9 | -620.60 | 1259.19 | |||
| (1 | Month) | 8 | -626.34 | 1268.67 | 11.48 | 1 | 0.00 |
| (1 | SiteID) | 8 | -620.60 | 1257.19 | 0.00 | 1 | 1.00 |
TA_models_0<-list("Inshore" = TA_in_0,
"Mid channel" = TA_mc_0,
"Offshore" = TA_of_0,
"Ocean" = TA_oc_0)
modelsummary(TA_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'TA model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 1.046 | 0.873 | 0.560 | 1.525 |
| (4.079) | (2.661) | (1.124) | (1.002) | |
| Intercept | 266.964 | 616.763 | 1249.936 | −695.347 |
| (8230.631) | (5370.342) | (2267.304) | (2021.206) | |
| Upper Keys | 555.392 | 2035.391 | −1299.384 | |
| (10657.499) | (7552.355) | (2877.320) | ||
| Middle Keys | −17995.336+ | −8041.978 | −1874.329 | −355.634 |
| (10334.207) | (6699.927) | (2851.984) | (2388.676) | |
| Lower Keys | −1234.489 | 1139.413 | −1257.505 | 674.419 |
| (10357.074) | (6732.364) | (2707.705) | (2401.637) | |
| Num.Obs. | 375 | 350 | 427 | 163 |
| R2 Marg. | 0.150 | 0.073 | 0.035 | 0.080 |
| R2 Cond. | 0.526 | 0.325 | ||
| AIC | 4245.5 | 3656.2 | 3734.2 | 1259.2 |
| BIC | 4288.7 | 3698.7 | 3778.8 | 1287.0 |
| ICC | 0.4 | 0.3 | ||
| RMSE | 66.44 | 42.86 | 18.44 | 10.59 |
| + p |
#modelsummary(TA_models, estimate = "p.value")Factors removed models
TA_models<-list("Inshore" = TA_in,
"Mid channel" = TA_mc,
"Offshore" = TA_of,
"Ocean" = TA_oc)
modelsummary(TA_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'TA model outputs (dropped facators)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 1.159* | |||
| (0.512) | ||||
| Intercept | 2377.924*** | 2365.447*** | 41.558 | 2381.578*** |
| (19.544) | (9.358) | (1032.895) | (1.612) | |
| Upper Keys | 1.656 | −0.539 | ||
| (10.851) | (2.891) | |||
| Middle Keys | −31.623** | −6.246* | ||
| (10.639) | (2.862) | |||
| Lower Keys | −87.269*** | −5.220+ | ||
| (10.552) | (2.713) | |||
| Num.Obs. | 375 | 350 | 427 | 163 |
| R2 Marg. | 0.136 | 0.000 | 0.030 | 0.000 |
| R2 Cond. | 0.516 | 0.322 | 0.169 | 0.141 |
| AIC | 4262.9 | 3690.6 | 3733.0 | 1269.7 |
| BIC | 4286.4 | 3706.0 | 3761.4 | 1278.9 |
| ICC | 0.4 | 0.3 | 0.1 | 0.1 |
| RMSE | 67.93 | 43.08 | 18.45 | 11.03 |
| + p |
#modelsummary(TA_models, estimate = "p.value")DIC-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$DIC_umol_kg
## W = 0.90821, p-value < 2.2e-16
Inshore
hist(Trends.inshore$DIC_umol_kg)
shapiro.test(Trends.inshore$DIC_umol_kg)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$DIC_umol_kg
## W = 0.99705, p-value = 0.7394# Inshore DIC
DIC_in<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(DIC_in)
anova(DIC_in) ranova(DIC_in) step(DIC_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -2114.4 4250.7
## (1 | Month) 0 10 -2240.7 4501.3 252.580 1 <2e-16 ***
## (1 | SiteID) 0 10 -2116.5 4253.0 4.252 1 0.0392 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 25312 8437 3 347.80 1.7086 0.16492
## Year 0 31161 31161 1 358.73 6.2722 0.01271 *
## Sub_region 0 95613 31871 3 7.24 6.4151 0.01913 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Sub_region + (1 | Month) + (1 | SiteID)DIC_in<-lmer(DIC_umol_kg ~ Year + Sub_region +
(1|Month) + (1|SiteID), data=Trends.inshore)
summary(DIC_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 4248.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4161 -0.6586 0.0303 0.6553 2.7863
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6626.4 81.40
## SiteID (Intercept) 219.1 14.80
## Residual 4968.1 70.48
## Number of obs: 373, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8737.835 4311.479 358.756 -2.027 0.04344 *
## Year 5.350 2.136 358.733 2.504 0.01271 *
## Sub_regionUK -16.641 17.467 7.250 -0.953 0.37140
## Sub_regionMK -44.133 17.314 7.009 -2.549 0.03812 *
## Sub_regionLK -68.049 17.283 6.958 -3.937 0.00569 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.035 0.033
## Sub_reginMK -0.010 0.008 0.588
## Sub_reginLK -0.018 0.016 0.589 0.595 plot(DIC_in)
qqnorm(resid(DIC_in))
acf(resid(DIC_in), main="acf(resid(DIC Inshore))") 
DIC_in.emmc<-emmeans(DIC_in, ~Sub_region+Year)
DIC_in.emmc<-multcomp::cld(DIC_in.emmc)
DIC_in.emmc<-DIC_in.emmc[order(DIC_in.emmc$Sub_region),]
DIC_in.emmcMid channel
hist(Trends.midchanel$DIC_umol_kg)
shapiro.test(Trends.midchanel$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$DIC_umol_kg
## W = 0.94836, p-value = 1.109e-09DIC_mc<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(DIC_mc)
anova(DIC_mc) ranova(DIC_mc) step(DIC_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1833.5 3689.0
## (1 | SiteID) 1 10 -1834.1 3688.1 1.085 1 0.2975
## (1 | Month) 0 9 -1891.7 3801.3 115.233 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 8033 2677.6 3 329.04 1.1682 0.321879
## Year 0 11372 11371.7 1 342.72 4.9528 0.026698 *
## Sub_region 0 34301 11433.8 3 332.14 4.9799 0.002156 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Sub_region + (1 | Month)DIC_mc<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(DIC_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3684.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2953 -0.4748 0.0154 0.5750 3.7927
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1395 37.34
## Residual 2296 47.92
## Number of obs: 348, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4589.568 2984.771 342.723 -1.538 0.125054
## Year 3.291 1.479 342.719 2.225 0.026698 *
## Sub_regionUK -7.413 8.314 332.145 -0.892 0.373229
## Sub_regionMK -21.935 7.358 332.068 -2.981 0.003083 **
## Sub_regionLK -24.601 7.343 332.174 -3.350 0.000901 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.012 0.011
## Sub_reginMK -0.002 0.000 0.530
## Sub_reginLK -0.018 0.016 0.532 0.601 plot(DIC_mc)
qqnorm(resid(DIC_mc))
acf(resid(DIC_mc), main="acf(resid(DIC Mid Channel))") 
DIC_mc.emmc<-emmeans(DIC_mc, ~Sub_region+Year)
DIC_mc.emmc<-multcomp::cld(DIC_mc.emmc)
DIC_mc.emmc<-DIC_mc.emmc[order(DIC_mc.emmc$Sub_region),]
DIC_mc.emmcOffshore
hist(Trends.offshore$DIC_umol_kg)
shapiro.test(Trends.offshore$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$DIC_umol_kg
## W = 0.95542, p-value = 4.729e-10DIC_of<-lmer(DIC_umol_kg~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(DIC_of)
anova(DIC_of) ranova(DIC_of) step(DIC_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1932.8 3887.7
## (1 | SiteID) 1 10 -1932.8 3885.7 0.00 1 1
## (1 | Month) 0 9 -1963.0 3944.0 60.28 1 8.226e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 806.3 268.8 3 407.55 0.5178 0.67026
## Year 0 23228.6 23228.6 1 411.35 44.9099 6.813e-11 ***
## Sub_region 0 4742.8 1580.9 3 410.76 3.0565 0.02825 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Sub_region + (1 | Month)DIC_of<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(DIC_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3874.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9981 -0.5363 0.1094 0.5639 2.6471
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 116.7 10.80
## Residual 517.2 22.74
## Number of obs: 426, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6415.8631 1263.6062 411.3193 -5.077 5.81e-07 ***
## Year 4.1960 0.6261 411.3550 6.701 6.81e-11 ***
## Sub_regionUK -2.7095 3.5029 410.5267 -0.774 0.4397
## Sub_regionMK -8.5696 3.4683 410.5117 -2.471 0.0139 *
## Sub_regionLK -0.6895 3.2882 410.9233 -0.210 0.8340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.004 0.002
## Sub_reginMK -0.004 0.003 0.591
## Sub_reginLK -0.013 0.011 0.625 0.632 plot(DIC_of)
qqnorm(resid(DIC_of))
acf(resid(DIC_of), main="acf(resid(DIC Offshore))") 
DIC_of.emmc<-emmeans(DIC_of, ~Sub_region+Year)
DIC_of.emmc<-multcomp::cld(DIC_of.emmc)
DIC_of.emmc<-DIC_of.emmc[order(DIC_of.emmc$Sub_region),]
DIC_of.emmcOceanic
hist(Trends.ocean$DIC_umol_kg)
shapiro.test(Trends.ocean$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$DIC_umol_kg
## W = 0.97946, p-value = 0.01698DIC_oc<-lmer(DIC_umol_kg ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(DIC_oc)
anova(DIC_oc) ranova(DIC_oc) step(DIC_oc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -684.21 1386.4
## (1 | SiteID) 1 8 -684.21 1384.4 0.0000 1 1.00000
## (1 | Month) 0 7 -687.08 1388.2 5.7341 1 0.01664 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 119.2 59.6 2 146.26 0.1898 0.8273
## Sub_region 2 158.1 79.0 2 148.05 0.2546 0.7756
## Year 0 13827.2 13827.2 1 151.57 45.0044 3.671e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + (1 | Month)DIC_oc<-lmer(DIC_umol_kg ~ Year+ (1 | Month), data = Trends.ocean)
summary(DIC_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1383.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54294 -0.63178 -0.03277 0.54011 2.77577
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 35.49 5.958
## Residual 307.24 17.528
## Number of obs: 161, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7873.046 1479.171 151.554 -5.323 3.62e-07 ***
## Year 4.917 0.733 151.572 6.709 3.67e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 plot(DIC_oc)
qqnorm(resid(DIC_oc))
acf(resid(DIC_oc), main="acf(resid(DIC open))")
DIC_oc.emmc<-emmeans(DIC_oc, ~Year)
DIC_oc.emmc<-multcomp::cld(DIC_oc.emmc)
#DIC_oc.emmc<-DIC_oc.emmc[order(DIC_oc.emmc$Sub_region),]
DIC_oc.emmcModels summary
DIC_models<-list("Inshore" = DIC_in,
"Mid channel" = DIC_mc,
"Offshore" = DIC_of,
"Ocean" = DIC_oc)
modelsummary(DIC_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'DIC model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 5.350* | 3.291* | 4.196*** | 4.917*** |
| (2.136) | (1.479) | (0.626) | (0.733) | |
| Intercept | −8737.835* | −4589.568 | −6415.863*** | −7873.046*** |
| (4311.479) | (2984.771) | (1263.606) | (1479.171) | |
| Upper Keys | −16.641 | −7.413 | −2.710 | |
| (17.467) | (8.314) | (3.503) | ||
| Middle Keys | −44.133* | −21.935** | −8.570* | |
| (17.314) | (7.358) | (3.468) | ||
| Lower Keys | −68.049*** | −24.601*** | −0.690 | |
| (17.283) | (7.343) | (3.288) | ||
| Num.Obs. | 373 | 348 | 426 | 161 |
| R2 Marg. | 0.061 | 0.038 | 0.116 | 0.230 |
| R2 Cond. | 0.605 | 0.401 | 0.279 | 0.309 |
| AIC | 4264.5 | 3698.2 | 3888.8 | 1391.4 |
| BIC | 4295.8 | 3725.2 | 3917.2 | 1403.7 |
| ICC | 0.6 | 0.4 | 0.2 | 0.1 |
| RMSE | 68.58 | 46.85 | 22.35 | 17.06 |
| + p |
#modelsummary(DIC_models, estimate = "p.value")dTA-All
Long_plot2<- ggplot(Reef.Trends.data) +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank())
##
## Shapiro-Wilk normality test
##
## data: Reef.Trends.data$dTA_MY
## W = 0.81068, p-value < 2.2e-16
Inshore
# Inshore dTA
hist(Trends.inshore$dTA_MY)
shapiro.test(Trends.inshore$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$dTA_MY
## W = 0.96647, p-value = 1.722e-07dTA_in<-lmer(dTA_MY ~ Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(dTA_in)
anova(dTA_in) ranova(dTA_in) step(dTA_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -2070.3 4162.7
## (1 | SiteID) 1 10 -2071.1 4162.3 1.563 1 0.2112
## (1 | Month) 0 9 -2149.7 4317.4 157.089 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 23417 7806 3 349.98 1.7157 0.1634
## Year 2 4185 4185 1 362.89 0.9143 0.3396
## Sub_region 0 503930 167977 3 353.92 36.7310 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ Sub_region + (1 | Month)dTA_in<-lmer(dTA_MY ~ Sub_region + (1 | Month),
data=Trends.inshore)
summary(dTA_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 4165.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5706 -0.5791 0.0397 0.5825 3.2500
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 3901 62.46
## Residual 4573 67.63
## Number of obs: 369, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.053 19.782 14.400 -0.154 0.8795
## Sub_regionUK 1.901 10.751 353.895 0.177 0.8597
## Sub_regionMK -32.076 10.489 353.896 -3.058 0.0024 **
## Sub_regionLK -87.830 10.382 353.949 -8.460 7.14e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.304
## Sub_reginMK -0.312 0.573
## Sub_reginLK -0.316 0.578 0.595 plot(dTA_in)
qqnorm(resid(dTA_in))
acf(resid(dTA_in), main="acf(resid(dTA Inshore))")
dTA_in.emmc<-emmeans(dTA_in, ~Sub_region)
dTA_in.emmc<-multcomp::cld(dTA_in.emmc)
dTA_in.emmc<-dTA_in.emmc[order(dTA_in.emmc$Sub_region),]
dTA_in.emmcMid channel
hist(Trends.midchanel$dTA_MY)
shapiro.test(Trends.midchanel$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$dTA_MY
## W = 0.83138, p-value < 2.2e-16dTA_mc<-lmer(dTA_MY~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(dTA_mc)
anova(dTA_mc) ranova(dTA_mc) step(dTA_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1785.4 3592.9
## (1 | Month) 0 10 -1824.0 3668.1 77.191 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1787.5 3594.9 4.048 1 0.04423 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 6411.6 2137.2 3 322.19 1.2124 0.30521
## Year 2 9.9 9.9 1 336.32 0.0056 0.94029
## Sub_region 3 24315.0 8105.0 3 6.24 4.6010 0.05089 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ (1 | Month) + (1 | SiteID)dTA_mc<-lmer(dTA_MY ~ (1 | Month) + (1|SiteID),
data = Trends.midchanel)
summary(dTA_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ (1 | Month) + (1 | SiteID)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3617.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0331 -0.3800 0.1133 0.5555 3.4580
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 767.2 27.70
## SiteID (Intercept) 240.9 15.52
## Residual 1761.7 41.97
## Number of obs: 347, groups: Month, 12; SiteID, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -16.062 9.679 16.151 -1.66 0.116 plot(dTA_mc)
qqnorm(resid(dTA_mc))
acf(resid(dTA_mc), main="acf(resid(dTA Mid Channel))") 
Offshore
hist(Trends.offshore$dTA_MY)
shapiro.test(Trends.offshore$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$dTA_MY
## W = 0.81414, p-value < 2.2e-16dTA_of<-lmer(dTA_MY~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(dTA_of)
anova(dTA_of) ranova(dTA_of) step(dTA_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1762.3 3546.5
## (1 | SiteID) 1 10 -1762.3 3544.5 0.000 1 1
## (1 | Month) 0 9 -1784.5 3587.0 44.459 1 2.597e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 163.74 54.58 3 402.83 0.2192 0.8831
## Year 2 382.27 382.27 1 404.60 1.5439 0.2148
## Sub_region 0 2792.28 930.76 3 407.46 3.7460 0.0112 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ Sub_region + (1 | Month)dTA_of<-lmer(dTA_MY ~ Sub_region + (1 | Month),
data =Trends.offshore)
summary(dTA_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3532.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7134 -0.3128 0.1093 0.5378 3.7524
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 47.53 6.894
## Residual 248.47 15.763
## Number of obs: 422, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.1008 2.7538 28.9761 -0.400 0.6923
## Sub_regionUK -0.2335 2.4524 407.0094 -0.095 0.9242
## Sub_regionMK -5.8844 2.4333 407.0245 -2.418 0.0160 *
## Sub_regionLK -4.9534 2.2996 407.7183 -2.154 0.0318 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.525
## Sub_reginMK -0.531 0.595
## Sub_reginLK -0.564 0.630 0.636 plot(dTA_of)
qqnorm(resid(dTA_of))
acf(resid(dTA_of), main="acf(resid(dTA Offshore))") 
dTA_of.emmc<-emmeans(dTA_of, ~Sub_region)
dTA_of.emmc<-multcomp::cld(dTA_of.emmc)
dTA_of.emmc<-dTA_of.emmc[order(dTA_of.emmc$Sub_region),]
dTA_of.emmcModels summary
dTA_models<-list("Inshore" = dTA_in,
"Mid channel" = dTA_mc,
"Offshore" = dTA_of)
modelsummary(dTA_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'dTA model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | |
|---|---|---|---|
| Intercept | −3.053 | −16.062+ | −1.101 |
| (19.782) | (9.679) | (2.754) | |
| Upper Keys | 1.901 | −0.233 | |
| (10.751) | (2.452) | ||
| Middle Keys | −32.076** | −5.884* | |
| (10.489) | (2.433) | ||
| Lower Keys | −87.830*** | −4.953* | |
| (10.382) | (2.300) | ||
| Num.Obs. | 369 | 347 | 422 |
| R2 Marg. | 0.140 | 0.000 | 0.022 |
| R2 Cond. | 0.536 | 0.364 | 0.179 |
| AIC | 4177.9 | 3625.7 | 3544.2 |
| BIC | 4201.4 | 3641.1 | 3568.4 |
| ICC | 0.5 | 0.4 | 0.2 |
| RMSE | 66.28 | 40.84 | 15.51 |
| + p |
#modelsummary(dTA_models, estimate = "p.value")dDIC-All
##
## Shapiro-Wilk normality test
##
## data: Reef.Trends.data$dDIC_MY
## W = 0.8692, p-value < 2.2e-16
Inshore
# Inshore dDIC
hist(Trends.inshore$dDIC_MY)
shapiro.test(Trends.inshore$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$dDIC_MY
## W = 0.99195, p-value = 0.04425dDIC_in<-lmer(dDIC_MY~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(dDIC_in)
anova(dDIC_in) ranova(dDIC_in) step(dDIC_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -2063.4 4148.7
## (1 | Month) 0 10 -2186.1 4392.3 245.533 1 < 2e-16 ***
## (1 | SiteID) 0 10 -2066.2 4152.3 5.591 1 0.01805 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 25267 8422.5 3 341.81 1.8782 0.1330
## Year 2 1 1.3 1 352.47 0.0003 0.9863
## Sub_region 0 90826 30275.4 3 7.26 6.7192 0.0169 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Sub_region + (1 | Month) + (1 | SiteID)dDIC_in<-lmer(dDIC_MY ~ Sub_region + (1 | Month)+ (1 | SiteID),
data=Trends.inshore)
summary(dDIC_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 4149.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4747 -0.6231 0.0291 0.6380 2.9028
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6077.1 77.96
## SiteID (Intercept) 241.3 15.53
## Residual 4505.8 67.13
## Number of obs: 367, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 10.259 26.313 16.255 0.390 0.70170
## Sub_regionUK -16.022 17.754 7.330 -0.902 0.39551
## Sub_regionMK -44.964 17.592 7.073 -2.556 0.03745 *
## Sub_regionLK -70.184 17.548 7.002 -3.999 0.00519 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.395
## Sub_reginMK -0.399 0.590
## Sub_reginLK -0.400 0.591 0.597 plot(dDIC_in)
qqnorm(resid(dDIC_in))
acf(resid(dDIC_in), main="acf(resid(dDIC Inshore))") 
dDIC_in.emmc<-emmeans(dDIC_in, ~Sub_region)
dDIC_in.emmc<-multcomp::cld(dDIC_in.emmc)
dDIC_in.emmc<-dDIC_in.emmc[order(dDIC_in.emmc$Sub_region),]
dDIC_in.emmcMid channel
hist(Trends.midchanel$dDIC_MY)
shapiro.test(Trends.midchanel$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$dDIC_MY
## W = 0.89741, p-value = 1.698e-14dDIC_mc<-lmer(dDIC_MY~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(dDIC_mc)
anova(dDIC_mc) ranova(dDIC_mc) step(dDIC_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1780.2 3582.4
## (1 | SiteID) 1 10 -1781.4 3582.8 2.36 1 0.1244
## (1 | Month) 0 9 -1842.3 3702.7 121.87 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 8833 2944.4 3 326.01 1.6029 0.1885586
## Year 2 4982 4981.7 1 339.38 2.6964 0.1015017
## Sub_region 0 36387 12128.9 3 330.09 6.5221 0.0002686 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Sub_region + (1 | Month)dDIC_mc<-lmer(dDIC_MY ~ Sub_region + (1 | Month),
data = Trends.midchanel)
summary(dDIC_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3584.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4652 -0.4651 0.0496 0.5297 3.9540
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1185 34.43
## Residual 1860 43.12
## Number of obs: 345, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.351 11.248 15.909 0.298 0.769611
## Sub_regionUK -9.260 7.525 330.051 -1.230 0.219398
## Sub_regionMK -22.939 6.690 330.028 -3.429 0.000683 ***
## Sub_regionLK -26.218 6.662 330.093 -3.936 0.000101 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.319
## Sub_reginMK -0.360 0.535
## Sub_reginLK -0.362 0.538 0.607 plot(dDIC_mc) 
qqnorm(resid(dDIC_mc))
acf(resid(dDIC_mc), main="acf(resid(dDIC Mid Channel))") 
dDIC_mc.emmc<-emmeans(dDIC_mc, ~Sub_region)
dDIC_mc.emmc<-multcomp::cld(dDIC_mc.emmc)
dDIC_mc.emmc<-dDIC_mc.emmc[order(dDIC_mc.emmc$Sub_region),]
dDIC_mc.emmcOffshore
hist(Trends.offshore$dDIC_MY)
shapiro.test(Trends.offshore$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$dDIC_MY
## W = 0.78379, p-value < 2.2e-16dDIC_of<-lmer(dDIC_MY~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(dDIC_of)
anova(dDIC_of) ranova(dDIC_of) step(dDIC_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1741.1 3504.3
## (1 | SiteID) 1 10 -1741.1 3502.3 0.000 1 1
## (1 | Month) 0 9 -1755.0 3527.9 27.641 1 1.46e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 1001.5 333.84 3 402.26 1.4401 0.230586
## Year 0 1594.1 1594.11 1 381.54 6.8540 0.009197 **
## Sub_region 0 3812.6 1270.86 3 405.62 5.4641 0.001087 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Year + Sub_region + (1 | Month)dDIC_of<-lmer(dDIC_MY ~ Year+ Sub_region + (1 | Month),
data =Trends.offshore)
summary(dDIC_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3491.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4443 -0.3349 0.1161 0.5360 3.8120
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 30.53 5.525
## Residual 232.58 15.251
## Number of obs: 421, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2188.848 835.522 381.460 2.620 0.009151 **
## Year -1.084 0.414 381.538 -2.618 0.009197 **
## Sub_regionUK -2.824 2.373 405.232 -1.190 0.234631
## Sub_regionMK -8.251 2.354 405.265 -3.505 0.000508 ***
## Sub_regionLK -1.524 2.225 405.813 -0.685 0.493829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.005 0.003
## Sub_reginMK -0.005 0.003 0.595
## Sub_reginLK -0.014 0.012 0.630 0.636 plot(dDIC_of)
qqnorm(resid(dDIC_of))
acf(resid(dDIC_of), main="acf(resid(dDIC Offshore))") 
dDIC_of.emmc<-emmeans(dDIC_of, ~Sub_region+Year)
dDIC_of.emmc<-multcomp::cld(dDIC_of.emmc)
dDIC_of.emmc<-dDIC_of.emmc[order(dDIC_of.emmc$Sub_region),]
dDIC_of.emmcModels summary
dDIC_models<-list("Inshore" = dDIC_in,
"Mid channel" = dDIC_mc,
"Offshore" = dDIC_of)
modelsummary(dDIC_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'dDIC model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | |
|---|---|---|---|
| Year | −1.084** | ||
| (0.414) | |||
| Intercept | 10.259 | 3.351 | 2188.848** |
| (26.313) | (11.248) | (835.522) | |
| Upper Keys | −16.022 | −9.260 | −2.824 |
| (17.754) | (7.525) | (2.373) | |
| Middle Keys | −44.964* | −22.939*** | −8.251*** |
| (17.592) | (6.690) | (2.354) | |
| Lower Keys | −70.184*** | −26.218*** | −1.524 |
| (17.548) | (6.662) | (2.225) | |
| Num.Obs. | 367 | 345 | 421 |
| R2 Marg. | 0.061 | 0.034 | 0.051 |
| R2 Cond. | 0.609 | 0.410 | 0.161 |
| AIC | 4163.9 | 3596.6 | 3505.7 |
| BIC | 4191.3 | 3619.6 | 3534.0 |
| ICC | 0.6 | 0.4 | 0.1 |
| RMSE | 65.35 | 42.22 | 15.00 |
| + p |
#modelsummary(dDIC_models, estimate = "p.value")Figure 2: Trends
Raw data lm
Trends_H<-ggarrange(Trends_a+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Trends_b+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
Trends_c + theme(legend.position = "bottom",
strip.text.x = element_blank()),
labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(0.9, 0.9, 1.2))
Trends_H
Trends_H2<-ggarrange(Trends_a2+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Trends_b2+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
Trends_c2 + theme(legend.position = "bottom",
strip.text.x = element_blank()),
labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(0.9, 0.9, 1.2))
Trends_H2
TrendsV<-ggarrange(Trends_a+
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
Trends_b+
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
Trends_c +
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
labels = c("A", "B", "C"),
ncol = 3, nrow = 1)
#TrendsV
#ggsave(file="FLK_results/Fig_2_Trends.svg", plot=TrendsH, dpi = 300, width=7.5, height=7)Figure 2: Ocean acidification trends in the Florida Reef Tract. (A) The partial pressure of the CO2, (B) pH, and (C) Aragonite saturation state (Ωar). The colors represent the biogeographical regions and the panels separate the locations by shelf zone. Text on the top of each panel shows the coefficient of change in the parameter per year (for significant trends) and the significance of the region in the model. Note the different scales between the top and bottom panels.
Trends_Modeled<-ggarrange(
Trends_pCO2,
Trends_pH,
Trends_Omega,
#labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(1.1, 0.9, 1.1))
Trends_Modeled
#ggsave(file="FLK_results/Fig_2_Trends.svg", plot=Trends_Modeled, dpi = 300, width=7.5, height=7)
#ggsave(file="FLK_results/Fig_2_Trends_ICRS.svg", plot=Trends_Modeled, dpi = 300, width=9, height=5)Figure 2: Ocean acidification trends in the Florida Reef Tract. (A) The partial pressure of the CO2, (B) pH, and (C) Aragonite saturation state (Ωar). The colors represent the biogeographical regions and the panels separate the locations by shelf zone. Text on the top of each panel shows the coefficient of change in the parameter per year (for significant trends). Solid lines represent models with significant trends (year<0.05). Multiple coloured lines are depicted for models where region was significant (region p<0.05). Dashed lines represent the intercepts of models with non significant trends (year p>0.05).
7. PCA
#str(Month_Stats)
PCA_variables<-c("DIC_mean", "TA_mean", "pCO2_mean", "Ara_mean",
"pH_mean", "Tem_mean","Sal_mean", "Season",
"Zone", "Sub_region", "Month2")
PCA.data<-select(Month_Stats, PCA_variables)
Name<-paste(Month_Stats$Zone, Month_Stats$Sub_region,
Month_Stats$Month, sep = "_")
rownames(PCA.data) <- (Name)
PCA_var.names<-c("DIC", "TA", "pCO2", "Ara",
"pH", "Temp","Sal", "Season",
"Zone", "Sub_region", "Month")
colnames(PCA.data) <- (PCA_var.names)
#ggpairs(PCA.data)head(PCA.data)library(vegan)
#library(autoplot)
library(ggfortify)
CC.rda1 <- rda(PCA.data[, -(6:11)], scale=TRUE)
CC.rda2 <- prcomp(PCA.data[, -(6:11)], scale=TRUE)
summary(CC.rda1)##
## Call:
## rda(X = PCA.data[, -(6:11)], scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 5 1
## Unconstrained 5 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Eigenvalue 2.5944 1.9403 0.45711 0.006922 0.0012585
## Proportion Explained 0.5189 0.3881 0.09142 0.001384 0.0002517
## Cumulative Proportion 0.5189 0.9069 0.99836 0.999748 1.0000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 5.469602
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5
## DIC -1.1029 2.1822 -0.03542 -0.008113 -0.061935
## TA 0.1238 2.3756 -0.56714 -0.010744 0.054366
## pCO2 -2.3089 -0.5250 -0.59657 0.143727 0.005462
## Ara 2.0089 -0.4769 -1.31126 0.002960 -0.026257
## pH 2.2192 0.8374 0.58039 0.143427 -0.004362
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5
## Inshore_BB_Jan -0.272379 0.691922 0.464656 -0.1099471 -0.158317
## Inshore_BB_Feb 0.272418 0.600475 0.320829 0.2191620 -0.312778
## Inshore_BB_Mar 0.380946 0.511217 -0.015298 0.2823851 -0.093517
## Inshore_BB_Apr 0.850344 -0.237717 0.026057 0.5966009 0.177635
## Inshore_BB_May 0.746874 -0.136414 -0.216369 0.4658404 0.011938
## Inshore_BB_Jun 0.285798 -0.908645 0.524958 -0.4155568 -0.166686
## Inshore_BB_Jul -0.444217 -0.487107 -0.212568 0.0041688 0.543124
## Inshore_BB_Aug -0.341170 -0.441671 -0.452599 0.5537129 0.216457
## Inshore_BB_Sep -1.174026 0.032383 -0.193326 1.8622707 0.206118
## Inshore_BB_Oct -1.171340 0.216051 -0.264913 1.5234470 0.218991
## Inshore_BB_Nov -0.675844 0.449297 0.192224 0.1885164 0.187854
## Inshore_BB_Dec -0.271227 0.561826 0.388899 -0.1141498 -0.183539
## Inshore_UK_Jan -0.131216 0.919059 0.319806 0.1006433 -0.053077
## Inshore_UK_Feb 0.609058 0.398344 0.279700 0.6700555 -0.251988
## Inshore_UK_Mar 0.537766 0.402503 -0.069266 0.4029285 0.358694
## Inshore_UK_Apr 1.481157 -0.591785 -0.175134 1.9849146 0.310744
## Inshore_UK_May 1.232421 -0.526323 -0.203991 1.1017521 0.419967
## Inshore_UK_Jun 0.469914 -0.414224 -0.176666 -0.2824107 0.015573
## Inshore_UK_Jul -0.074839 -0.383048 -0.468384 -0.2350545 0.080163
## Inshore_UK_Aug 0.286982 -0.328415 -0.829756 -0.3915844 0.014936
## Inshore_UK_Sep -0.920822 -0.373659 -0.325180 1.2251027 0.113247
## Inshore_UK_Oct -1.060306 0.036598 -0.213666 1.4718463 0.153896
## Inshore_UK_Nov -1.069601 0.699378 -0.042354 1.0301854 0.893107
## Inshore_UK_Dec -0.387882 0.851172 0.114440 0.0368029 0.310689
## Inshore_MK_Jan -0.315480 0.895362 0.258144 -0.0705074 0.183153
## Inshore_MK_Feb 0.619871 0.786041 -0.062712 1.0056499 -1.178989
## Inshore_MK_Mar 0.411287 0.414342 -0.087735 0.1827799 0.491210
## Inshore_MK_Apr 1.108327 -0.317746 -0.149535 1.1268399 0.356214
## Inshore_MK_May 1.223427 -0.659784 -0.064130 1.1703070 0.533580
## Inshore_MK_Jun 0.523935 -1.035346 0.329953 -0.1140607 -0.001052
## Inshore_MK_Jul -0.289009 -1.216137 0.439818 -0.4096161 0.460619
## Inshore_MK_Aug 0.036473 -1.074496 0.338568 -0.6610479 0.093846
## Inshore_MK_Sep -0.648288 -0.773930 0.280517 0.1338367 0.442284
## Inshore_MK_Oct -0.498062 -0.146284 -0.033121 0.5370888 -0.643452
## Inshore_MK_Nov -0.498429 0.117859 0.347102 -0.0729214 0.301566
## Inshore_MK_Dec -0.287707 0.806998 0.221431 0.0081968 -0.031416
## Inshore_LK_Jan -0.656640 0.312186 1.046642 -0.1405027 0.309660
## Inshore_LK_Feb 0.110192 0.621121 0.353034 0.1523841 -0.518591
## Inshore_LK_Mar 0.426923 0.191538 0.228841 0.3705963 0.146184
## Inshore_LK_Apr 0.626584 -0.649475 0.585984 0.2895094 -0.349235
## Inshore_LK_May 0.631593 -1.217018 1.155171 -0.0273152 -0.023007
## Inshore_LK_Jun 0.060518 -1.083452 0.681062 -0.3814186 0.181271
## Inshore_LK_Jul -0.720779 -1.255383 0.707658 0.1131456 0.122450
## Inshore_LK_Aug -0.576415 -1.582907 0.963565 0.2283386 -2.100300
## Inshore_LK_Sep -0.801218 -0.872523 0.505717 0.4680002 0.132707
## Inshore_LK_Oct -0.739680 -0.744149 0.793617 0.3529505 -0.258240
## Inshore_LK_Nov -0.963632 -0.033628 0.524101 0.2763751 0.958829
## Inshore_LK_Dec -0.304766 -0.015814 1.069776 -0.2795579 -0.210587
## Mid channel_BB_Jan 0.027816 0.271188 0.420160 -0.2164743 -0.005563
## Mid channel_BB_Feb 0.107344 0.316050 0.495185 -0.1044365 -0.188670
## Mid channel_BB_Mar 0.247549 0.288581 0.243455 -0.0943901 0.187354
## Mid channel_BB_Apr 0.373367 0.052513 0.069195 -0.0471094 0.157799
## Mid channel_BB_May 0.324871 0.152536 -0.171991 -0.1565114 0.170819
## Mid channel_BB_Jun 0.122118 -0.182032 -0.150988 -0.3165015 -0.201615
## Mid channel_BB_Jul -0.261711 -0.103029 -0.512555 -0.0906459 0.180654
## Mid channel_BB_Aug -0.162639 -0.232497 -0.556464 -0.2932580 0.252667
## Mid channel_BB_Sep -0.650792 -0.038128 -0.201716 0.4754130 -0.186922
## Mid channel_BB_Oct -0.329703 0.019930 -0.273383 0.0253423 -0.319908
## Mid channel_BB_Nov -0.362413 0.189511 0.020472 0.0035264 0.364634
## Mid channel_BB_Dec -0.080520 0.330699 0.293850 -0.2116488 0.004395
## Mid channel_UK_Jan 0.139588 0.395990 0.319130 -0.0676589 -0.137564
## Mid channel_UK_Feb 0.309461 0.267720 0.503656 0.1372748 -0.405322
## Mid channel_UK_Mar 0.444319 0.142571 0.206860 0.1918618 0.108416
## Mid channel_UK_Apr 0.635788 0.045489 -0.191015 0.3748800 0.123618
## Mid channel_UK_May 0.705328 0.108866 -0.431302 0.4232428 0.343377
## Mid channel_UK_Jun 0.178812 -0.050278 -0.426803 -0.3229466 0.140829
## Mid channel_UK_Jul -0.163013 -0.186142 -0.540347 -0.1661891 -0.110097
## Mid channel_UK_Aug 0.142560 0.050587 -0.976053 -0.3617340 0.126345
## Mid channel_UK_Sep -0.488549 -0.090577 -0.484462 0.5766830 -0.247703
## Mid channel_UK_Oct -0.301796 -0.016799 -0.336452 0.0565071 -0.543577
## Mid channel_UK_Nov -0.477884 0.092964 0.211991 0.0475302 0.121392
## Mid channel_UK_Dec -0.027189 0.336005 0.265682 -0.2273655 0.092068
## Mid channel_MK_Jan -0.132939 0.597376 0.197684 -0.1568954 0.201887
## Mid channel_MK_Feb 0.315885 0.335899 0.408721 0.1592351 -0.339990
## Mid channel_MK_Mar 0.396814 0.316764 0.122129 0.2429641 0.310896
## Mid channel_MK_Apr 0.617345 0.087571 -0.212291 0.2010248 0.213396
## Mid channel_MK_May 0.717589 -0.263968 -0.140637 0.3503768 0.317073
## Mid channel_MK_Jun 0.237239 -0.595405 0.037210 -0.4174343 -0.051692
## Mid channel_MK_Jul -0.216268 -0.675125 -0.100763 -0.3147962 0.034242
## Mid channel_MK_Aug 0.152739 -0.452837 -0.440989 -0.4937739 -0.071266
## Mid channel_MK_Sep -0.413916 -0.467108 -0.045401 -0.0595721 -0.253919
## Mid channel_MK_Oct -0.132252 0.011830 -0.368464 -0.0580431 -0.603035
## Mid channel_MK_Nov -0.294367 0.076062 0.165701 -0.1151497 0.080504
## Mid channel_MK_Dec -0.128313 0.433207 0.310124 -0.1271917 0.018153
## Mid channel_LK_Jan -0.441394 0.304326 0.742244 -0.1426029 -0.028110
## Mid channel_LK_Feb 0.147012 0.576346 0.269111 0.1143538 -0.246307
## Mid channel_LK_Mar 0.162114 0.310472 0.182092 -0.0899937 0.085535
## Mid channel_LK_Apr 0.220247 -0.118015 0.157447 0.2557912 0.138649
## Mid channel_LK_May 0.443824 -0.630229 0.631471 -0.2024468 0.125221
## Mid channel_LK_Jun 0.064426 -0.356803 -0.013168 -0.3739680 0.132184
## Mid channel_LK_Jul -0.322013 -0.500906 -0.200915 -0.1430501 0.045734
## Mid channel_LK_Aug -0.139133 -0.719313 0.021488 -0.4455852 2.596236
## Mid channel_LK_Sep -0.403774 -0.514397 0.050025 -0.0608102 -0.369386
## Mid channel_LK_Oct -0.209240 -0.124067 -0.189483 -0.1195314 -0.447048
## Mid channel_LK_Nov -0.397931 0.038183 0.112575 -0.0435787 0.316707
## Mid channel_LK_Dec -0.011135 0.181993 0.402097 -0.2073895 -0.048307
## Offshore_BB_Jan 0.094639 0.202050 0.257713 -0.1616951 -0.250127
## Offshore_BB_Feb 0.246880 0.187418 0.517195 -0.0002223 -0.277095
## Offshore_BB_Mar 0.247177 0.232596 0.244117 -0.1080296 0.195279
## Offshore_BB_Apr 0.061421 0.170950 0.087916 -0.2697911 0.222204
## Offshore_BB_May 0.177506 0.158261 -0.098723 -0.2101719 0.034345
## Offshore_BB_Jun -0.117707 -0.011749 -0.233372 -0.2549729 0.197366
## Offshore_BB_Jul -0.149370 -0.057170 -0.558836 -0.1281561 -0.187325
## Offshore_BB_Aug -0.306713 -0.185217 -0.517237 -0.1358155 0.405680
## Offshore_BB_Sep -0.241875 -0.017887 -0.547435 0.1705399 -0.558985
## Offshore_BB_Oct -0.041980 -0.035943 -0.373132 -0.1447452 -0.624485
## Offshore_BB_Nov -0.163689 0.140543 -0.128061 -0.1006554 0.318614
## Offshore_BB_Dec 0.020843 0.197247 0.195626 -0.1968915 -0.018085
## Offshore_UK_Jan 0.117996 0.224795 0.268254 -0.2017194 0.013086
## Offshore_UK_Feb 0.206530 0.262528 0.584675 0.0365769 -0.428452
## Offshore_UK_Mar 0.203353 0.227080 0.217806 -0.1751998 0.203339
## Offshore_UK_Apr 0.087257 0.156344 0.106252 -0.2353006 0.157848
## Offshore_UK_May 0.123547 0.199670 -0.148077 -0.2726268 0.305191
## Offshore_UK_Jun -0.020314 -0.010433 -0.298227 -0.3324183 0.241518
## Offshore_UK_Jul -0.182954 -0.159520 -0.511902 -0.1373704 -0.085680
## Offshore_UK_Aug 0.073760 0.014392 -0.857112 -0.3624673 0.182729
## Offshore_UK_Sep -0.131902 -0.077189 -0.625520 -0.0453484 -0.488111
## Offshore_UK_Oct 0.049137 -0.006270 -0.393591 -0.1610509 -0.690637
## Offshore_UK_Nov -0.328974 0.029951 0.016280 -0.0272213 0.214979
## Offshore_UK_Dec -0.011484 0.253316 0.159294 -0.2167585 0.090499
## Offshore_MK_Jan 0.020292 0.239692 0.311475 -0.2483381 0.039121
## Offshore_MK_Feb 0.212486 0.287005 0.640178 0.0711030 -0.461947
## Offshore_MK_Mar 0.167949 0.245177 0.227452 -0.1946883 0.249919
## Offshore_MK_Apr 0.211927 0.144859 0.051341 -0.1827855 0.169375
## Offshore_MK_May 0.117455 0.136568 -0.106852 -0.2508365 0.280804
## Offshore_MK_Jun 0.174030 -0.137077 -0.237590 -0.3399719 0.012379
## Offshore_MK_Jul -0.176367 -0.311803 -0.328304 -0.1615133 -0.245292
## Offshore_MK_Aug 0.083749 -0.083170 -0.711071 -0.3574560 0.004723
## Offshore_MK_Sep -0.142458 -0.183522 -0.396511 -0.1274628 -0.477251
## Offshore_MK_Oct 0.017719 0.002412 -0.429455 -0.1287964 -0.712757
## Offshore_MK_Nov -0.125116 0.084615 -0.123884 -0.0657433 0.150421
## Offshore_MK_Dec 0.070349 0.163956 0.250276 -0.1637809 -0.112147
## Offshore_LK_Jan 0.002444 0.279240 0.414633 -0.2451106 0.061644
## Offshore_LK_Feb 0.103299 0.310233 0.718745 0.0250108 -0.482889
## Offshore_LK_Mar 0.141527 0.168049 0.183831 -0.2263034 0.273595
## Offshore_LK_Apr -0.026695 0.078487 0.196372 -0.2320677 0.277058
## Offshore_LK_May 0.023278 0.092947 0.082945 -0.3535737 0.340662
## Offshore_LK_Jun -0.040452 -0.054511 -0.233011 -0.3527097 0.230755
## Offshore_LK_Jul -0.366320 -0.130999 -0.445877 0.0485103 0.132517
## Offshore_LK_Aug -0.135899 -0.047940 -0.614103 -0.2489012 0.404367
## Offshore_LK_Sep -0.187357 -0.112903 -0.365389 -0.0319935 -0.528833
## Offshore_LK_Oct -0.054760 -0.017707 -0.314172 -0.1962809 -0.415250
## Offshore_LK_Nov -0.261344 0.084855 -0.046039 -0.0607739 0.355848
## Offshore_LK_Dec 0.105968 0.162302 0.204457 -0.1056861 -0.219242
## Oceanic_UK_Jan 0.116386 0.145657 0.147585 -0.2276401 -0.062608
## Oceanic_UK_Feb 0.224225 0.265908 0.575611 0.0207100 -0.216071
## Oceanic_UK_Mar 0.210853 0.108698 0.272847 -0.1727647 0.001050
## Oceanic_UK_Apr 0.230648 0.146471 0.181060 -0.1283766 -0.064051
## Oceanic_UK_May 0.285901 0.216495 -0.149354 -0.2047512 0.304087
## Oceanic_UK_Jun 0.110768 0.056801 -0.386743 -0.3233744 0.177900
## Oceanic_UK_Jul -0.094032 -0.134068 -0.536872 -0.0335767 -0.710588
## Oceanic_UK_Aug 0.122940 0.184028 -0.977464 -0.3513756 0.322607
## Oceanic_UK_Sep -0.005012 -0.057522 -0.620110 -0.0254629 -0.874835
## Oceanic_UK_Oct -0.001904 -0.038419 -0.454190 -0.1528981 -0.607495
## Oceanic_UK_Nov -0.362185 0.084373 -0.037581 0.0726912 0.074331
## Oceanic_UK_Dec 0.093812 0.211356 0.061470 -0.1905821 0.120498
## Oceanic_MK_Jan 0.103012 0.207417 0.251834 -0.2071099 -0.029856
## Oceanic_MK_Feb 0.290080 0.216463 0.475611 0.0578686 -0.298683
## Oceanic_MK_Mar 0.124129 0.125321 0.176655 -0.2767160 0.179863
## Oceanic_MK_Apr 0.009943 0.148176 0.084632 -0.2743630 0.286291
## Oceanic_MK_May 0.013544 0.201969 -0.107842 -0.2742015 0.293429
## Oceanic_MK_Jun 0.085494 0.036501 -0.392986 -0.3361585 0.260028
## Oceanic_MK_Jul -0.119019 -0.099287 -0.539476 -0.1003937 -0.336104
## Oceanic_MK_Aug 0.036292 0.043633 -0.779012 -0.2959192 -0.015420
## Oceanic_MK_Sep -0.028008 0.021147 -0.646887 -0.0757960 -0.497093
## Oceanic_MK_Oct -0.006473 0.041053 -0.522417 -0.1677384 -0.407504
## Oceanic_MK_Nov 0.015388 0.086324 -0.206021 -0.1855881 0.289422
## Oceanic_MK_Dec 0.064452 0.127672 0.230045 -0.1757171 -0.118489
## Oceanic_LK_Jan 0.075786 0.228659 0.316479 -0.2422946 0.120143
## Oceanic_LK_Feb 0.259839 0.295997 0.515907 0.0707476 -0.198802
## Oceanic_LK_Mar 0.146556 0.103057 0.140167 -0.2564721 0.142702
## Oceanic_LK_Apr -0.014314 0.100364 0.150169 -0.2946073 0.169104
## Oceanic_LK_May 0.020027 0.170524 -0.008745 -0.3283284 0.312733
## Oceanic_LK_Jun 0.036178 0.048539 -0.355358 -0.3119397 0.238326
## Oceanic_LK_Jul -0.228284 -0.062666 -0.498783 -0.0218039 -0.305378
## Oceanic_LK_Aug -0.048204 0.022345 -0.713934 -0.2451638 -0.365497
## Oceanic_LK_Sep 0.009807 -0.026111 -0.588335 -0.0117892 -0.735383
## Oceanic_LK_Oct -0.005716 -0.008146 -0.405671 -0.1939036 -0.481968
## Oceanic_LK_Nov -0.147811 0.123518 -0.185444 -0.0305197 0.213744
## Oceanic_LK_Dec 0.103015 0.139055 0.140125 -0.1210380 -0.278429# Checking if the PC axes are meaningful
eigenval <- CC.rda1$CA$eig # Here you will get the eigenvalues
sitecoord <- CC.rda1$CA$u[,1:2] # The site coordinates along PC1 and PC2
eig <- data.frame(eigenval)
eig$nb <- c(1:length(eigenval))
eig$prop <- eig$eigenval/sum(eig$eigenval)
eig# (Kaiser-Guttman)
par(mfrow=c(1,2))
barplot(eig$eigenval, main="Eigenvalues",las=2)
abline(h=mean(eig$eigenval),col="red") # average eigenvalue
legend("topright","Average eigenvalue",lwd=1,col=2,bty="n")
barplot(100*eig$prop,main="% of variance",las=2)
par(mfrow=c(1,1))
autoplot(CC.rda2, data = PCA.data, colour="Season",
shape="Sub_region",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Zone)+MyTheme+Season_colour+Season_fill
autoplot(CC.rda2, data = PCA.data, colour="Zone",
shape="Sub_region",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Season)+MyTheme
autoplot(CC.rda2, data = PCA.data, colour="Sub_region",
shape="Zone",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_grid(~Season)+MyTheme
autoplot(CC.rda2, data = PCA.data, colour="Season",
shape="Zone",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Sub_region)+MyTheme+Season_colour+Season_fill
Packages used
# Creates bibliography
#knitr::write_bib(c(.packages()), "packages.bib")
Arel-Bundock, Vincent. 2022. Modelsummary: Summary Tables and Plots
for Statistical Models and Data: Beautiful, Customizable, and
Publication-Ready. https://vincentarelbundock.github.io/modelsummary/.
Arnold, Jeffrey B. 2021. Ggthemes: Extra Themes, Scales and Geoms
for Ggplot2. https://github.com/jrnold/ggthemes.
Attali, Dean, and Christopher Baker. 2019. ggExtra: Add Marginal
Histograms to Ggplot2, and More Ggplot2 Enhancements. https://github.com/daattali/ggExtra.
Auguie, Baptiste. 2017. gridExtra: Miscellaneous Functions for
"Grid" Graphics. https://CRAN.R-project.org/package=gridExtra.
Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. 2015.
“Fitting Linear Mixed-Effects Models Using lme4.” Journal of Statistical
Software 67 (1): 1–48. https://doi.org/10.18637/jss.v067.i01.
Bates, Douglas, and Martin Maechler. 2021. Matrix: Sparse and Dense
Matrix Classes and Methods. http://Matrix.R-forge.R-project.org/.
Bates, Douglas, Martin Maechler, Ben Bolker, and Steven Walker. 2021.
Lme4: Linear Mixed-Effects Models Using Eigen and S4. https://github.com/lme4/lme4/.
Cheng, Joe, Bhaskar Karambelkar, and Yihui Xie. 2021. Leaflet:
Create Interactive Web Maps with the JavaScript Leaflet Library. https://rstudio.github.io/leaflet/.
Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times
Made Easy with lubridate.”
Journal of Statistical Software 40 (3): 1–25. https://www.jstatsoft.org/v40/i03/.
Henry, Lionel, and Hadley Wickham. 2020. Purrr: Functional
Programming Tools. https://CRAN.R-project.org/package=purrr.
J, Lemon. 2006. “Plotrix: A Package in the Red Light District of
r.” R-News 6 (4): 8–12.
Kassambara, Alboukadel. 2020. Ggpubr: Ggplot2 Based Publication
Ready Plots. https://rpkgs.datanovia.com/ggpubr/.
Kuznetsova, Alexandra, Per B. Brockhoff, and Rune H. B. Christensen.
2017. “lmerTest Package: Tests in
Linear Mixed Effects Models.” Journal of Statistical
Software 82 (13): 1–26. https://doi.org/10.18637/jss.v082.i13.
Kuznetsova, Alexandra, Per Bruun Brockhoff, and Rune Haubo Bojesen
Christensen. 2019. lmerTest: Tests in Linear Mixed Effects
Models. https://github.com/runehaubo/lmerTestR.
Legendre, Pierre. 2018. Lmodel2: Model II Regression. https://CRAN.R-project.org/package=lmodel2.
Lemon, Jim, Ben Bolker, Sander Oom, Eduardo Klein, Barry Rowlingson,
Hadley Wickham, Anupam Tyagi, et al. 2021. Plotrix: Various Plotting
Functions. https://CRAN.R-project.org/package=plotrix.
Lenth, Russell V. 2022. Emmeans: Estimated Marginal Means, Aka
Least-Squares Means. https://github.com/rvlenth/emmeans.
Müller, Kirill, and Hadley Wickham. 2021. Tibble: Simple Data
Frames. https://CRAN.R-project.org/package=tibble.
R Core Team. 2020. R: A Language and Environment for Statistical
Computing. Vienna, Austria: R Foundation for Statistical Computing.
https://www.R-project.org/.
Robinson, David, Alex Hayes, and Simon Couch. 2022. Broom: Convert
Statistical Objects into Tidy Tibbles. https://CRAN.R-project.org/package=broom.
Spinu, Vitalie, Garrett Grolemund, and Hadley Wickham. 2021.
Lubridate: Make Dealing with Dates a Little Easier. https://CRAN.R-project.org/package=lubridate.
Wickham, Hadley. 2007. “Reshaping Data with the reshape Package.” Journal of
Statistical Software 21 (12): 1–20. http://www.jstatsoft.org/v21/i12/.
———. 2011. “The Split-Apply-Combine Strategy for Data
Analysis.” Journal of Statistical Software 40 (1): 1–29.
http://www.jstatsoft.org/v40/i01/.
———. 2016. Ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag New York. https://ggplot2.tidyverse.org.
———. 2019. Stringr: Simple, Consistent Wrappers for Common String
Operations. https://CRAN.R-project.org/package=stringr.
———. 2020a. Plyr: Tools for Splitting, Applying and Combining
Data. https://CRAN.R-project.org/package=plyr.
———. 2020b. Reshape2: Flexibly Reshape Data: A Reboot of the Reshape
Package. https://github.com/hadley/reshape.
———. 2021a. Forcats: Tools for Working with Categorical Variables
(Factors). https://CRAN.R-project.org/package=forcats.
———. 2021b. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.
———. 2021c. Tidyverse: Easily Install and Load the Tidyverse.
https://CRAN.R-project.org/package=tidyverse.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy
D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019.
“Welcome to the tidyverse.”
Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen,
Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey
Dunnington. 2021. Ggplot2: Create Elegant Data Visualisations Using
the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2021.
Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, and Jim Hester. 2020. Readr: Read Rectangular Text
Data. https://CRAN.R-project.org/package=readr.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible
Research in R.” In Implementing Reproducible
Computational Research, edited by Victoria Stodden, Friedrich
Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595.
———. 2015. Dynamic Documents with R and Knitr. 2nd
ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.
———. 2021. Knitr: A General-Purpose Package for Dynamic Report
Generation in r. https://yihui.org/knitr/.
Zhu, Hao. 2021. kableExtra: Construct Complex Table with Kable and
Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.